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Stergios Christodoulidis
ARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland

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Journal article
Published: 30 July 2021 in JMIR mHealth and uHealth
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Background Digital technologies have evolved dramatically in recent years, finding applications in a variety of aspects of everyday life. Smartphones and mobile apps are being used for a steadily increasing number of tasks, including health monitoring. A large number of nutrition and diet apps are available, and some of them are very popular in terms of user downloads, highlighting a trend toward diet monitoring and assessment. Objective We sought to explore the perspectives of end users on the features, current use, and acceptance of nutrition and diet mHealth apps with a survey. We expect that this study can provide user insights to assist researchers and developers in achieving innovative dietary assessments. Methods A multidisciplinary team designed and compiled the survey. Before its release, it was pilot-tested by 18 end users. A 19-question survey was finally developed and was translated into six languages: English, German, French, Spanish, Italian, and Greek. The participants were mainly recruited via social media platforms and mailing lists of universities, university hospitals, and patient associations. Results A total of 2382 respondents (1891 female, 79.4%; 474 male, 19.9%; and 17 neither, 0.7%) with a mean age of 27.2 years (SD 8.5) completed the survey. Approximately half of the participants (1227/2382, 51.5%) had used a nutrition and diet app. The primary criteria for selecting such an app were ease of use (1570/2382, 65.9%), free cost (1413/2382, 59.3%), and ability to produce automatic readings of caloric content (1231/2382, 51.7%) and macronutrient content (1117/2382, 46.9%) (ie, food type and portion size are estimated by the system without any contribution from the user). An app was less likely to be selected if it incorrectly estimated portion size, calories, or nutrient content (798/2382, 33.5%). Other important limitations included the use of a database that does not include local foods (655/2382, 27.5%) or that may omit major foods (977/2382, 41%). Conclusions This comprehensive study in a mostly European population assessed the preferences and perspectives of potential nutrition and diet app users. Understanding user needs will benefit researchers who develop tools for innovative dietary assessment as well as those who assist research on behavioral changes related to nutrition.

ACS Style

Maria F Vasiloglou; Stergios Christodoulidis; Emilie Reber; Thomai Stathopoulou; Ya Lu; Zeno Stanga; Stavroula Mougiakakou. Perspectives and Preferences of Adult Smartphone Users Regarding Nutrition and Diet Apps: Web-Based Survey Study. JMIR mHealth and uHealth 2021, 9, e27885 .

AMA Style

Maria F Vasiloglou, Stergios Christodoulidis, Emilie Reber, Thomai Stathopoulou, Ya Lu, Zeno Stanga, Stavroula Mougiakakou. Perspectives and Preferences of Adult Smartphone Users Regarding Nutrition and Diet Apps: Web-Based Survey Study. JMIR mHealth and uHealth. 2021; 9 (7):e27885.

Chicago/Turabian Style

Maria F Vasiloglou; Stergios Christodoulidis; Emilie Reber; Thomai Stathopoulou; Ya Lu; Zeno Stanga; Stavroula Mougiakakou. 2021. "Perspectives and Preferences of Adult Smartphone Users Regarding Nutrition and Diet Apps: Web-Based Survey Study." JMIR mHealth and uHealth 9, no. 7: e27885.

Journal article
Published: 07 May 2021 in Cell Death & Differentiation
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The prognosis of early breast cancer (BC) relies on cell autonomous and immune parameters. The impact of the intestinal microbiome on clinical outcome has not yet been evaluated. Shotgun metagenomics was used to determine the composition of the fecal microbiota in 121 specimens from 76 early BC patients, 45 of whom were paired before and after chemotherapy. These patients were enrolled in the CANTO prospective study designed to record the side effects associated with the clinical management of BC. We analyzed associations between baseline or post-chemotherapy fecal microbiota and plasma metabolomics with BC prognosis, as well as with therapy-induced side effects. We examined the clinical relevance of these findings in immunocompetent mice colonized with BC patient microbiota that were subsequently challenged with histo-compatible mouse BC and chemotherapy. We conclude that specific gut commensals that are overabundant in BC patients compared with healthy individuals negatively impact BC prognosis, are modulated by chemotherapy, and may influence weight gain and neurological side effects of BC therapies. These findings obtained in adjuvant and neoadjuvant settings warrant prospective validation.

ACS Style

Safae Terrisse; Lisa Derosa; Valerio Iebba; François Ghiringhelli; Ines Vaz-Luis; Guido Kroemer; Marine Fidelle; Stergios Christodoulidis; Nicola Segata; Andrew Maltez Thomas; Anne-Laure Martin; Aude Sirven; Sibille Everhard; Fanny Aprahamian; Nitharsshini Nirmalathasan; Romy Aarnoutse; Marjolein Smidt; Janine Ziemons; Carlos Caldas; Sibylle Loibl; Carsten Denkert; Sylvere Durand; Claudia Iglesias; Filippo Pietrantonio; Bertrand Routy; Fabrice André; Edoardo Pasolli; Suzette Delaloge; Laurence Zitvogel. Intestinal microbiota influences clinical outcome and side effects of early breast cancer treatment. Cell Death & Differentiation 2021, 1 -19.

AMA Style

Safae Terrisse, Lisa Derosa, Valerio Iebba, François Ghiringhelli, Ines Vaz-Luis, Guido Kroemer, Marine Fidelle, Stergios Christodoulidis, Nicola Segata, Andrew Maltez Thomas, Anne-Laure Martin, Aude Sirven, Sibille Everhard, Fanny Aprahamian, Nitharsshini Nirmalathasan, Romy Aarnoutse, Marjolein Smidt, Janine Ziemons, Carlos Caldas, Sibylle Loibl, Carsten Denkert, Sylvere Durand, Claudia Iglesias, Filippo Pietrantonio, Bertrand Routy, Fabrice André, Edoardo Pasolli, Suzette Delaloge, Laurence Zitvogel. Intestinal microbiota influences clinical outcome and side effects of early breast cancer treatment. Cell Death & Differentiation. 2021; ():1-19.

Chicago/Turabian Style

Safae Terrisse; Lisa Derosa; Valerio Iebba; François Ghiringhelli; Ines Vaz-Luis; Guido Kroemer; Marine Fidelle; Stergios Christodoulidis; Nicola Segata; Andrew Maltez Thomas; Anne-Laure Martin; Aude Sirven; Sibille Everhard; Fanny Aprahamian; Nitharsshini Nirmalathasan; Romy Aarnoutse; Marjolein Smidt; Janine Ziemons; Carlos Caldas; Sibylle Loibl; Carsten Denkert; Sylvere Durand; Claudia Iglesias; Filippo Pietrantonio; Bertrand Routy; Fabrice André; Edoardo Pasolli; Suzette Delaloge; Laurence Zitvogel. 2021. "Intestinal microbiota influences clinical outcome and side effects of early breast cancer treatment." Cell Death & Differentiation , no. : 1-19.

Preprint content
Published: 11 February 2021
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BACKGROUND Digital technologies have evolved dramatically in recent years, finding applications in a variety of aspects of everyday life. Smartphones and mobile apps are being used for a steadily increasing number of tasks, including health monitoring. A large number of nutrition and diet apps are available, and some of them are very popular in terms of user downloads, highlighting a trend toward diet monitoring and assessment. OBJECTIVE We sought to explore the perspectives of end users on the features, current use, and acceptance of nutrition and diet mHealth apps with a survey. We expect that this study can provide user insights to assist researchers and developers in achieving innovative dietary assessments. METHODS A multidisciplinary team designed and compiled the survey. Before its release, it was pilot-tested by 18 end users. A 19-question survey was finally developed and was translated into six languages: English, German, French, Spanish, Italian, and Greek. The participants were mainly recruited via social media platforms and mailing lists of universities, university hospitals, and patient associations. RESULTS A total of 2382 respondents (1891 female, 79.4%; 474 male, 19.9%; and 17 neither, 0.7%) with a mean age of 27.2 years (SD 8.5) completed the survey. Approximately half of the participants (1227/2382, 51.5%) had used a nutrition and diet app. The primary criteria for selecting such an app were ease of use (1570/2382, 65.9%), free cost (1413/2382, 59.3%), and ability to produce automatic readings of caloric content (1231/2382, 51.7%) and macronutrient content (1117/2382, 46.9%) (ie, food type and portion size are estimated by the system without any contribution from the user). An app was less likely to be selected if it incorrectly estimated portion size, calories, or nutrient content (798/2382, 33.5%). Other important limitations included the use of a database that does not include local foods (655/2382, 27.5%) or that may omit major foods (977/2382, 41%). CONCLUSIONS This comprehensive study in a mostly European population assessed the preferences and perspectives of potential nutrition and diet app users. Understanding user needs will benefit researchers who develop tools for innovative dietary assessment as well as those who assist research on behavioral changes related to nutrition.

ACS Style

Maria F Vasiloglou; Stergios Christodoulidis; Emilie Reber; Thomai Stathopoulou; Ya Lu; Zeno Stanga; Stavroula Mougiakakou. Perspectives and Preferences of Adult Smartphone Users Regarding Nutrition and Diet Apps: Web-Based Survey Study (Preprint). 2021, 1 .

AMA Style

Maria F Vasiloglou, Stergios Christodoulidis, Emilie Reber, Thomai Stathopoulou, Ya Lu, Zeno Stanga, Stavroula Mougiakakou. Perspectives and Preferences of Adult Smartphone Users Regarding Nutrition and Diet Apps: Web-Based Survey Study (Preprint). . 2021; ():1.

Chicago/Turabian Style

Maria F Vasiloglou; Stergios Christodoulidis; Emilie Reber; Thomai Stathopoulou; Ya Lu; Zeno Stanga; Stavroula Mougiakakou. 2021. "Perspectives and Preferences of Adult Smartphone Users Regarding Nutrition and Diet Apps: Web-Based Survey Study (Preprint)." , no. : 1.

Journal article
Published: 15 October 2020 in Medical Image Analysis
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Coronavirus disease 2019 (COVID-19) emerged in 2019 and disseminated around the world rapidly. Computed tomography (CT) imaging has been proven to be an important tool for screening, disease quantification and staging. The latter is of extreme importance for organizational anticipation (availability of intensive care unit beds, patient management planning) as well as to accelerate drug development through rapid, reproducible and quantified assessment of treatment response. Even if currently there are no specific guidelines for the staging of the patients, CT together with some clinical and biological biomarkers are used. In this study, we collected a multi-center cohort and we investigated the use of medical imaging and artificial intelligence for disease quantification, staging and outcome prediction. Our approach relies on automatic deep learning-based disease quantification using an ensemble of architectures, and a data-driven consensus for the staging and outcome prediction of the patients fusing imaging biomarkers with clinical and biological attributes. Highly promising results on multiple external/independent evaluation cohorts as well as comparisons with expert human readers demonstrate the potentials of our approach.

ACS Style

Guillaume Chassagnon; Maria Vakalopoulou; Enzo Battistella; Stergios Christodoulidis; Trieu-Nghi Hoang-Thi; Severine Dangeard; Eric Deutsch; Fabrice Andre; Enora Guillo; Nara Halm; Stefany El Hajj; Florian Bompard; Sophie Neveu; Chahinez Hani; Ines Saab; Aliénor Campredon; Hasmik Koulakian; Souhail Bennani; Gael Freche; Maxime Barat; Aurelien Lombard; Laure Fournier; Hippolyte Monnier; Téodor Grand; Jules Gregory; Yann Nguyen; Antoine Khalil; Elyas Mahdjoub; Pierre-Yves Brillet; Stéphane Tran Ba; Valérie Bousson; Ahmed Mekki; Robert-Yves Carlier; Marie-Pierre Revel; Nikos Paragios. AI-driven quantification, staging and outcome prediction of COVID-19 pneumonia. Medical Image Analysis 2020, 67, 101860 -101860.

AMA Style

Guillaume Chassagnon, Maria Vakalopoulou, Enzo Battistella, Stergios Christodoulidis, Trieu-Nghi Hoang-Thi, Severine Dangeard, Eric Deutsch, Fabrice Andre, Enora Guillo, Nara Halm, Stefany El Hajj, Florian Bompard, Sophie Neveu, Chahinez Hani, Ines Saab, Aliénor Campredon, Hasmik Koulakian, Souhail Bennani, Gael Freche, Maxime Barat, Aurelien Lombard, Laure Fournier, Hippolyte Monnier, Téodor Grand, Jules Gregory, Yann Nguyen, Antoine Khalil, Elyas Mahdjoub, Pierre-Yves Brillet, Stéphane Tran Ba, Valérie Bousson, Ahmed Mekki, Robert-Yves Carlier, Marie-Pierre Revel, Nikos Paragios. AI-driven quantification, staging and outcome prediction of COVID-19 pneumonia. Medical Image Analysis. 2020; 67 ():101860-101860.

Chicago/Turabian Style

Guillaume Chassagnon; Maria Vakalopoulou; Enzo Battistella; Stergios Christodoulidis; Trieu-Nghi Hoang-Thi; Severine Dangeard; Eric Deutsch; Fabrice Andre; Enora Guillo; Nara Halm; Stefany El Hajj; Florian Bompard; Sophie Neveu; Chahinez Hani; Ines Saab; Aliénor Campredon; Hasmik Koulakian; Souhail Bennani; Gael Freche; Maxime Barat; Aurelien Lombard; Laure Fournier; Hippolyte Monnier; Téodor Grand; Jules Gregory; Yann Nguyen; Antoine Khalil; Elyas Mahdjoub; Pierre-Yves Brillet; Stéphane Tran Ba; Valérie Bousson; Ahmed Mekki; Robert-Yves Carlier; Marie-Pierre Revel; Nikos Paragios. 2020. "AI-driven quantification, staging and outcome prediction of COVID-19 pneumonia." Medical Image Analysis 67, no. : 101860-101860.

Journal article
Published: 24 July 2020 in Nutrients
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Accurate dietary assessment is crucial for both the prevention and treatment of nutrition-related diseases. Since mobile-based dietary assessment solutions are promising, we sought to examine the acceptability of ″Nutrition and Diet″ (ND) apps by Healthcare Professionals (HCP), explore their preferences on apps′ features and identify predictors of acceptance. A 23 question survey was developed by an interdisciplinary team and pilot-tested. The survey was completed by 1001 HCP from 73 countries and 6 continents. The HCP (dietitians: 833, doctors: 75, nurses: 62, other: 31/females: 847, males: 150, neither: 4) had a mean age (SD) of 34.4 (10.2) years and mean job experience in years (SD): 7.7 (8.2). There were 45.5% who have recommended ND apps to their clients/patients. Of those who have not yet recommended an app, 22.5% do not know of their existence. Important criteria for selecting an app were ease of use (87.1%), apps being free of charge (72.6%) and validated (69%). Significant barriers were the use of inaccurate food composition database (52%), lack of local food composition database support (48.2%) and tech-savviness (43.3%). Although the adoption of smartphones is growing and mobile health research is advancing, there is room for improvement in the recommendation of ND apps by HCP.

ACS Style

Maria F. Vasiloglou; Stergios Christodoulidis; Emilie Reber; Thomai Stathopoulou; Ya Lu; Zeno Stanga; Stavroula Mougiakakou. What Healthcare Professionals Think of ″Nutrition & Diet″ Apps: An International Survey. Nutrients 2020, 12, 2214 .

AMA Style

Maria F. Vasiloglou, Stergios Christodoulidis, Emilie Reber, Thomai Stathopoulou, Ya Lu, Zeno Stanga, Stavroula Mougiakakou. What Healthcare Professionals Think of ″Nutrition & Diet″ Apps: An International Survey. Nutrients. 2020; 12 (8):2214.

Chicago/Turabian Style

Maria F. Vasiloglou; Stergios Christodoulidis; Emilie Reber; Thomai Stathopoulou; Ya Lu; Zeno Stanga; Stavroula Mougiakakou. 2020. "What Healthcare Professionals Think of ″Nutrition & Diet″ Apps: An International Survey." Nutrients 12, no. 8: 2214.

Journal article
Published: 11 May 2020 in IEEE Transactions on Multimedia
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Regular monitoring of nutrient intake in hospitalised patients plays a critical role in reducing the risk of disease-related malnutrition. Although several methods to estimate nutrient intake have been developed, there is still a clear demand for a more reliable and fully automated technique, as this could improve data accuracy and reduce both the burden on participants and health costs. In this paper, we propose a novel system based on artificial intelligence (AI) to accurately estimate nutrient intake, by simply processing RGB Depth (RGB-D) image pairs captured before and after meal consumption. The system includes a novel multi-task contextual network for food segmentation, a few-shot learning-based classifier built by limited training samples for food recognition, and an algorithm for 3D surface construction. This allows sequential food segmentation, recognition, and estimation of the consumed food volume, permitting fully automatic estimation of the nutrient intake for each meal. For the development and evaluation of the system, a dedicated new database containing images and nutrient recipes of 322 meals is assembled, coupled to data annotation using innovative strategies. Experimental results demonstrate that the estimated nutrient intake is highly correlated (> 0.91) to the ground truth and shows very small mean relative errors (< 20%), outperforming existing techniques proposed for nutrient intake assessment.

ACS Style

Ya Lu; Thomai Stathopoulou; Maria Vasiloglou; Stergios Christodoulidis; Zeno Stanga; Stavroula Mougiakakou. An Artificial Intelligence-Based System to Assess Nutrient Intake for Hospitalised Patients. IEEE Transactions on Multimedia 2020, 23, 1136 -1147.

AMA Style

Ya Lu, Thomai Stathopoulou, Maria Vasiloglou, Stergios Christodoulidis, Zeno Stanga, Stavroula Mougiakakou. An Artificial Intelligence-Based System to Assess Nutrient Intake for Hospitalised Patients. IEEE Transactions on Multimedia. 2020; 23 (99):1136-1147.

Chicago/Turabian Style

Ya Lu; Thomai Stathopoulou; Maria Vasiloglou; Stergios Christodoulidis; Zeno Stanga; Stavroula Mougiakakou. 2020. "An Artificial Intelligence-Based System to Assess Nutrient Intake for Hospitalised Patients." IEEE Transactions on Multimedia 23, no. 99: 1136-1147.

Original research article
Published: 20 March 2020 in Frontiers in Computational Neuroscience
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Image registration and segmentation are the two most studied problems in medical image analysis. Deep learning algorithms have recently gained a lot of attention due to their success and state-of-the-art results in variety of problems and communities. In this paper, we propose a novel, efficient, and multi-task algorithm that addresses the problems of image registration and brain tumor segmentation jointly. Our method exploits the dependencies between these tasks through a natural coupling of their interdependencies during inference. In particular, the similarity constraints are relaxed within the tumor regions using an efficient and relatively simple formulation. We evaluated the performance of our formulation both quantitatively and qualitatively for registration and segmentation problems on two publicly available datasets (BraTS 2018 and OASIS 3), reporting competitive results with other recent state-of-the-art methods. Moreover, our proposed framework reports significant amelioration (p < 0.005) for the registration performance inside the tumor locations, providing a generic method that does not need any predefined conditions (e.g., absence of abnormalities) about the volumes to be registered. Our implementation is publicly available online at https://github.com/TheoEst/joint_registration_tumor_segmentation.

ACS Style

Théo Estienne; Marvin Lerousseau; Maria Vakalopoulou; Emilie Alvarez Andres; Enzo Battistella; Alexandre Carré; Siddhartha Chandra; Stergios Christodoulidis; Mihir Sahasrabudhe; Roger Sun; Charlotte Robert; Hugues Talbot; Nikos Paragios; Eric Deutsch. Deep Learning-Based Concurrent Brain Registration and Tumor Segmentation. Frontiers in Computational Neuroscience 2020, 14, 17 .

AMA Style

Théo Estienne, Marvin Lerousseau, Maria Vakalopoulou, Emilie Alvarez Andres, Enzo Battistella, Alexandre Carré, Siddhartha Chandra, Stergios Christodoulidis, Mihir Sahasrabudhe, Roger Sun, Charlotte Robert, Hugues Talbot, Nikos Paragios, Eric Deutsch. Deep Learning-Based Concurrent Brain Registration and Tumor Segmentation. Frontiers in Computational Neuroscience. 2020; 14 ():17.

Chicago/Turabian Style

Théo Estienne; Marvin Lerousseau; Maria Vakalopoulou; Emilie Alvarez Andres; Enzo Battistella; Alexandre Carré; Siddhartha Chandra; Stergios Christodoulidis; Mihir Sahasrabudhe; Roger Sun; Charlotte Robert; Hugues Talbot; Nikos Paragios; Eric Deutsch. 2020. "Deep Learning-Based Concurrent Brain Registration and Tumor Segmentation." Frontiers in Computational Neuroscience 14, no. : 17.

Review
Published: 13 January 2020 in PLoS ONE
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To conduct a meta-analysis to determine specific computed tomography (CT) patterns and clinical features that discriminate between nonspecific interstitial pneumonia (NSIP) and usual interstitial pneumonia (UIP). The PubMed/Medline and Embase databases were searched for studies describing the radiological patterns of UIP and NSIP in chest CT images. Only studies involving histologically confirmed diagnoses and a consensus diagnosis by an interstitial lung disease (ILD) board were included in this analysis. The radiological patterns and patient demographics were extracted from suitable articles. We used random-effects meta-analysis by DerSimonian & Laird and calculated pooled odds ratios for binary data and pooled mean differences for continuous data. Of the 794 search results, 33 articles describing 2,318 patients met the inclusion criteria. Twelve of these studies included both NSIP (338 patients) and UIP (447 patients). NSIP-patients were significantly younger (NSIP: median age 54.8 years, UIP: 59.7 years; mean difference (MD) -4.4; p = 0.001; 95% CI: -6.97 to -1.77), less often male (NSIP: median 52.8%, UIP: 73.6%; pooled odds ratio (OR) 0.32; p<0.001; 95% CI: 0.17 to 0.60), and less often smokers (NSIP: median 55.1%, UIP: 73.9%; OR 0.42; p = 0.005; 95% CI: 0.23 to 0.77) than patients with UIP. The CT findings from patients with NSIP revealed significantly lower levels of the honeycombing pattern (NSIP: median 28.9%, UIP: 73.4%; OR 0.07; p<0.001; 95% CI: 0.02 to 0.30) with less peripheral predominance (NSIP: median 41.8%, UIP: 83.3%; OR 0.21; p<0.001; 95% CI: 0.11 to 0.38) and more subpleural sparing (NSIP: median 40.7%, UIP: 4.3%; OR 16.3; p = 0.005; 95% CI: 2.28 to 117). Honeycombing with a peripheral predominance was significantly associated with a diagnosis of UIP. The NSIP pattern showed more subpleural sparing. The UIP pattern was predominantly observed in elderly males with a history of smoking, whereas NSIP occurred in a younger patient population.

ACS Style

Lukas Ebner; Stergios Christodoulidis; Thomai Stathopoulou; Thomas Geiser; Odile Stalder; Andreas Limacher; Johannes Heverhagen; Stavroula G. Mougiakakou; Andreas Christe. Meta-analysis of the radiological and clinical features of Usual Interstitial Pneumonia (UIP) and Nonspecific Interstitial Pneumonia (NSIP). PLoS ONE 2020, 15, e0226084 .

AMA Style

Lukas Ebner, Stergios Christodoulidis, Thomai Stathopoulou, Thomas Geiser, Odile Stalder, Andreas Limacher, Johannes Heverhagen, Stavroula G. Mougiakakou, Andreas Christe. Meta-analysis of the radiological and clinical features of Usual Interstitial Pneumonia (UIP) and Nonspecific Interstitial Pneumonia (NSIP). PLoS ONE. 2020; 15 (1):e0226084.

Chicago/Turabian Style

Lukas Ebner; Stergios Christodoulidis; Thomai Stathopoulou; Thomas Geiser; Odile Stalder; Andreas Limacher; Johannes Heverhagen; Stavroula G. Mougiakakou; Andreas Christe. 2020. "Meta-analysis of the radiological and clinical features of Usual Interstitial Pneumonia (UIP) and Nonspecific Interstitial Pneumonia (NSIP)." PLoS ONE 15, no. 1: e0226084.

Conference paper
Published: 10 October 2019 in Transactions on Petri Nets and Other Models of Concurrency XV
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In this study, we propose a 3D deep neural network called U-ReSNet, a joint framework that can accurately register and segment medical volumes. The proposed network learns to automatically generate linear and elastic deformation models, trained by minimizing the mean square error and the local cross correlation similarity metrics. In parallel, a coupled architecture is integrated, seeking to provide segmentation maps for anatomies or tissue patterns using an additional decoder part trained with the dice coefficient metric. U-ReSNet is trained in an end to end fashion, while due to this joint optimization the generated network features are more informative leading to promising results compared to other deep learning-based methods existing in the literature. We evaluated the proposed architecture using the publicly available OASIS 3 dataset, measuring the dice coefficient metric for both registration and segmentation tasks. Our promising results indicate the potentials of our method which is composed from a convolutional architecture that is extremely simple and light in terms of parameters.

ACS Style

Théo Estienne; Maria Vakalopoulou; Stergios Christodoulidis; Enzo Battistela; Marvin Lerousseau; Alexandre Carre; Guillaume Klausner; Roger Sun; Charlotte Robert; Stavroula Mougiakakou; Nikos Paragios; Eric Deutsch. U-ReSNet: Ultimate Coupling of Registration and Segmentation with Deep Nets. Transactions on Petri Nets and Other Models of Concurrency XV 2019, 310 -319.

AMA Style

Théo Estienne, Maria Vakalopoulou, Stergios Christodoulidis, Enzo Battistela, Marvin Lerousseau, Alexandre Carre, Guillaume Klausner, Roger Sun, Charlotte Robert, Stavroula Mougiakakou, Nikos Paragios, Eric Deutsch. U-ReSNet: Ultimate Coupling of Registration and Segmentation with Deep Nets. Transactions on Petri Nets and Other Models of Concurrency XV. 2019; ():310-319.

Chicago/Turabian Style

Théo Estienne; Maria Vakalopoulou; Stergios Christodoulidis; Enzo Battistela; Marvin Lerousseau; Alexandre Carre; Guillaume Klausner; Roger Sun; Charlotte Robert; Stavroula Mougiakakou; Nikos Paragios; Eric Deutsch. 2019. "U-ReSNet: Ultimate Coupling of Registration and Segmentation with Deep Nets." Transactions on Petri Nets and Other Models of Concurrency XV , no. : 310-319.

Article
Published: 01 October 2019 in Investigative Radiology
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The objective of this study is to assess the performance of a computer-aided diagnosis (CAD) system (INTACT system) for the automatic classification of high-resolution computed tomography images into 4 radiological diagnostic categories and to compare this with the performance of radiologists on the same task. For the comparison, a total of 105 cases of pulmonary fibrosis were studied (54 cases of nonspecific interstitial pneumonia and 51 cases of usual interstitial pneumonia). All diagnoses were interstitial lung disease board consensus diagnoses (radiologically or histologically proven cases) and were retrospectively selected from our database. Two subspecialized chest radiologists made a consensual ground truth radiological diagnosis, according to the Fleischner Society recommendations. A comparison analysis was performed between the INTACT system and 2 other radiologists with different years of experience (readers 1 and 2). The INTACT system consists of a sequential pipeline in which first the anatomical structures of the lung are segmented, then the various types of pathological lung tissue are identified and characterized, and this information is then fed to a random forest classifier able to recommend a radiological diagnosis. Reader 1, reader 2, and INTACT achieved similar accuracy for classifying pulmonary fibrosis into the original 4 categories: 0.6, 0.54, and 0.56, respectively, with P > 0.45. The INTACT system achieved an F-score (harmonic mean for precision and recall) of 0.56, whereas the 2 readers, on average, achieved 0.57 (P = 0.991). For the pooled classification (2 groups, with and without the need for biopsy), reader 1, reader 2, and CAD had similar accuracies of 0.81, 0.70, and 0.81, respectively. The F-score was again similar for the CAD system and the radiologists. The CAD system and the average reader reached F-scores of 0.80 and 0.79 (P = 0.898). We found that a computer-aided detection algorithm based on machine learning was able to classify idiopathic pulmonary fibrosis with similar accuracy to a human reader. This is an open-access article distributed under the terms of the Creative Commons Attribution-Non Commercial-No Derivatives License 4.0 (CCBY-NC-ND), where it is permissible to download and share the work provided it is properly cited. The work cannot be changed in any way or used commercially without permission from the journal.

ACS Style

Andreas Christe; Alan A. Peters; Dionysios Drakopoulos; Johannes Heverhagen; Thomas Geiser; Thomai Stathopoulou; Stergios Christodoulidis; Marios Anthimopoulos; Stavroula G. Mougiakakou; Lukas Ebner. Computer-Aided Diagnosis of Pulmonary Fibrosis Using Deep Learning and CT Images. Investigative Radiology 2019, 54, 627 -632.

AMA Style

Andreas Christe, Alan A. Peters, Dionysios Drakopoulos, Johannes Heverhagen, Thomas Geiser, Thomai Stathopoulou, Stergios Christodoulidis, Marios Anthimopoulos, Stavroula G. Mougiakakou, Lukas Ebner. Computer-Aided Diagnosis of Pulmonary Fibrosis Using Deep Learning and CT Images. Investigative Radiology. 2019; 54 (10):627-632.

Chicago/Turabian Style

Andreas Christe; Alan A. Peters; Dionysios Drakopoulos; Johannes Heverhagen; Thomas Geiser; Thomai Stathopoulou; Stergios Christodoulidis; Marios Anthimopoulos; Stavroula G. Mougiakakou; Lukas Ebner. 2019. "Computer-Aided Diagnosis of Pulmonary Fibrosis Using Deep Learning and CT Images." Investigative Radiology 54, no. 10: 627-632.

Conference paper
Published: 01 July 2019 in 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
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Regular nutrient intake monitoring in hospitalised patients plays a critical role in reducing the risk of disease-related malnutrition (DRM). Although several methods to estimate nutrient intake have been developed, there is still a clear demand for a more reliable and fully automated technique, as this could improve the data accuracy and reduce both the participant burden and the health costs. In this paper, we propose a novel system based on artificial intelligence to accurately estimate nutrient intake, by simply processing RGB depth image pairs captured before and after a meal consumption. For the development and evaluation of the system, a dedicated and new database of images and recipes of 322 meals was assembled, coupled to data annotation using innovative strategies. With this database, a system was developed that employed a novel multi-task neural network and an algorithm for 3D surface construction. This allowed sequential semantic food segmentation and estimation of the volume of the consumed food, and permitted fully automatic estimation of nutrient intake for each food type with a 15% estimation error.

ACS Style

Ya Lu; Thomai Stathopoulou; Maria Vasiloglou; Stergios Christodoulidis; Beat Blum; Thomas Walser; Vinzenz Meier; Zeno Stanga; Stavroula G. Mougiakakou. An Artificial Intelligence-Based System for Nutrient Intake Assessment of Hospitalised Patients*. 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) 2019, 5696 -5699.

AMA Style

Ya Lu, Thomai Stathopoulou, Maria Vasiloglou, Stergios Christodoulidis, Beat Blum, Thomas Walser, Vinzenz Meier, Zeno Stanga, Stavroula G. Mougiakakou. An Artificial Intelligence-Based System for Nutrient Intake Assessment of Hospitalised Patients*. 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). 2019; ():5696-5699.

Chicago/Turabian Style

Ya Lu; Thomai Stathopoulou; Maria Vasiloglou; Stergios Christodoulidis; Beat Blum; Thomas Walser; Vinzenz Meier; Zeno Stanga; Stavroula G. Mougiakakou. 2019. "An Artificial Intelligence-Based System for Nutrient Intake Assessment of Hospitalised Patients*." 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) , no. : 5696-5699.

Conference paper
Published: 01 January 2019 in Proceedings of the 5th International Workshop on Multimedia Assisted Dietary Management - MADiMa '19
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ACS Style

Matthias Fontanellaz; Stergios Christodoulidis; Stavroula Mougiakakou. Self-Attention and Ingredient-Attention Based Model for Recipe Retrieval from Image Queries. Proceedings of the 5th International Workshop on Multimedia Assisted Dietary Management - MADiMa '19 2019, 25 -31.

AMA Style

Matthias Fontanellaz, Stergios Christodoulidis, Stavroula Mougiakakou. Self-Attention and Ingredient-Attention Based Model for Recipe Retrieval from Image Queries. Proceedings of the 5th International Workshop on Multimedia Assisted Dietary Management - MADiMa '19. 2019; ():25-31.

Chicago/Turabian Style

Matthias Fontanellaz; Stergios Christodoulidis; Stavroula Mougiakakou. 2019. "Self-Attention and Ingredient-Attention Based Model for Recipe Retrieval from Image Queries." Proceedings of the 5th International Workshop on Multimedia Assisted Dietary Management - MADiMa '19 , no. : 25-31.

Conference paper
Published: 12 September 2018 in Transactions on Petri Nets and Other Models of Concurrency XV
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Image registration and in particular deformable registration methods are pillars of medical imaging. Inspired by the recent advances in deep learning, we propose in this paper, a novel convolutional neural network architecture that couples linear and deformable registration within a unified architecture endowed with near real-time performance. Our framework is modular with respect to the global transformation component, as well as with respect to the similarity function while it guarantees smooth displacement fields. We evaluate the performance of our network on the challenging problem of MRI lung registration, and demonstrate superior performance with respect to state of the art elastic registration methods. The proposed deformation (between inspiration & expiration) was considered within a clinically relevant task of interstitial lung disease (ILD) classification and showed promising results.

ACS Style

Christodoulidis Stergios; Sahasrabudhe Mihir; Vakalopoulou Maria; Chassagnon Guillaume; Revel Marie-Pierre; Mougiakakou Stavroula; Paragios Nikos. Linear and Deformable Image Registration with 3D Convolutional Neural Networks. Transactions on Petri Nets and Other Models of Concurrency XV 2018, 13 -22.

AMA Style

Christodoulidis Stergios, Sahasrabudhe Mihir, Vakalopoulou Maria, Chassagnon Guillaume, Revel Marie-Pierre, Mougiakakou Stavroula, Paragios Nikos. Linear and Deformable Image Registration with 3D Convolutional Neural Networks. Transactions on Petri Nets and Other Models of Concurrency XV. 2018; ():13-22.

Chicago/Turabian Style

Christodoulidis Stergios; Sahasrabudhe Mihir; Vakalopoulou Maria; Chassagnon Guillaume; Revel Marie-Pierre; Mougiakakou Stavroula; Paragios Nikos. 2018. "Linear and Deformable Image Registration with 3D Convolutional Neural Networks." Transactions on Petri Nets and Other Models of Concurrency XV , no. : 13-22.

Journal article
Published: 26 March 2018 in IEEE Journal of Biomedical and Health Informatics
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Early and accurate diagnosis of interstitial lung diseases (ILDs) is crucial for making treatment decisions, but can be challenging even for experienced radiologists. The diagnostic procedure is based on the detection and recognition of the different ILD pathologies in thoracic CT scans, yet their manifestation often appears similar. In this study, we propose the use of a deep purely convolutional neural network for the semantic segmentation of ILD patterns, as the basic component of a computer aided diagnosis (CAD) system for ILDs. The proposed CNN, which consists of convolutional layers with dilated filters, takes as input a lung CT image of arbitrary size and outputs the corresponding label map. We trained and tested the network on a dataset of 172 sparsely annotated CT scans, within a cross-validation scheme. The training was performed in an end-to-end and semi-supervised fashion, utilizing both labeled and non-labeled image regions. The experimental results show significant performance improvement with respect to the state of the art.

ACS Style

Marios M. Anthimopoulos; Stergios Christodoulidis; Lukas Ebner; Thomas Geiser; Andreas Christe; Stavroula G. Mougiakakou. Semantic Segmentation of Pathological Lung Tissue With Dilated Fully Convolutional Networks. IEEE Journal of Biomedical and Health Informatics 2018, 23, 714 -722.

AMA Style

Marios M. Anthimopoulos, Stergios Christodoulidis, Lukas Ebner, Thomas Geiser, Andreas Christe, Stavroula G. Mougiakakou. Semantic Segmentation of Pathological Lung Tissue With Dilated Fully Convolutional Networks. IEEE Journal of Biomedical and Health Informatics. 2018; 23 (2):714-722.

Chicago/Turabian Style

Marios M. Anthimopoulos; Stergios Christodoulidis; Lukas Ebner; Thomas Geiser; Andreas Christe; Stavroula G. Mougiakakou. 2018. "Semantic Segmentation of Pathological Lung Tissue With Dilated Fully Convolutional Networks." IEEE Journal of Biomedical and Health Informatics 23, no. 2: 714-722.

Conference paper
Published: 01 June 2017 in 2017 IEEE 30th International Symposium on Computer-Based Medical Systems (CBMS)
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An effective surveillance strategy for the progression of abdominal aortic aneurysms (AAAs) may be achieved by assessing its expected growth rate in a personalized manner. Given the variety of factors with an impact on AAA growth, an integrative approach to the problem could potentially benefit from incorporating clinical and morphometric data, as well as mechanical stress characterizations. In addition, here we investigated the use of texture information on computed tomography angiography images within the AAA sac. A cohort of n=38 patients underwent a baseline examination, plus a follow-up visit to measure AAA growth rates, in terms of its maximum diameter (Dmax) divided by the elapsed time period. Subsequently, each case was labelled as slow, medium or quick growth, compared to the expected rate reported in demographic studies, as a function of gender and baseline Dmax. We computed a total of 102 features (5 clinical, 17 morphometric, 4 biomechanical, and 76 on texture) and used a number of machine learning (ML) algorithms; with the aim of minimizing misclassification costs. The performance of the system was evaluated with a leave-one-out cross-validation scheme. The results achieved by the best performing approach, an ensemble of decision trees ('LPBoost') using the entire 102-dimensional feature space, indicated that the combination of different information sources, along with ML algorithms, may have a positive impact on the AAA prognosis assessment.

ACS Style

Fernando Garcia-Garcia; Eleni Metaxa; Stergios Christodoulidis; Marios Anthimopoulos; Nikolaos Kontopodis; Martina Correa-Londono; Thomas R. Wyss; Yannis Papaharilaou; Christos V. Ioannou; Hendrik von Tengg-Kobligk; Stavroula Mougiakakou. Prognosis of Abdominal Aortic Aneurysms: A Machine Learning-Enabled Approach Merging Clinical, Morphometric, Biomechanical and Texture Information. 2017 IEEE 30th International Symposium on Computer-Based Medical Systems (CBMS) 2017, 463 -468.

AMA Style

Fernando Garcia-Garcia, Eleni Metaxa, Stergios Christodoulidis, Marios Anthimopoulos, Nikolaos Kontopodis, Martina Correa-Londono, Thomas R. Wyss, Yannis Papaharilaou, Christos V. Ioannou, Hendrik von Tengg-Kobligk, Stavroula Mougiakakou. Prognosis of Abdominal Aortic Aneurysms: A Machine Learning-Enabled Approach Merging Clinical, Morphometric, Biomechanical and Texture Information. 2017 IEEE 30th International Symposium on Computer-Based Medical Systems (CBMS). 2017; ():463-468.

Chicago/Turabian Style

Fernando Garcia-Garcia; Eleni Metaxa; Stergios Christodoulidis; Marios Anthimopoulos; Nikolaos Kontopodis; Martina Correa-Londono; Thomas R. Wyss; Yannis Papaharilaou; Christos V. Ioannou; Hendrik von Tengg-Kobligk; Stavroula Mougiakakou. 2017. "Prognosis of Abdominal Aortic Aneurysms: A Machine Learning-Enabled Approach Merging Clinical, Morphometric, Biomechanical and Texture Information." 2017 IEEE 30th International Symposium on Computer-Based Medical Systems (CBMS) , no. : 463-468.

Journal article
Published: 07 December 2016 in IEEE Journal of Biomedical and Health Informatics
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Early diagnosis of interstitial lung diseases is crucial for their treatment, but even experienced physicians find it difficult, as their clinical manifestations are similar. In order to assist with the diagnosis, computer-aided diagnosis systems have been developed. These commonly rely on a fixed scale classifier that scans CT images, recognizes textural lung patterns, and generates a map of pathologies. In a previous study, we proposed a method for classifying lung tissue patterns using a deep convolutional neural network (CNN), with an architecture designed for the specific problem. In this study, we present an improved method for training the proposed network by transferring knowledge from the similar domain of general texture classification. Six publicly available texture databases are used to pretrain networks with the proposed architecture, which are then fine-tuned on the lung tissue data. The resulting CNNs are combined in an ensemble and their fused knowledge is compressed back to a network with the original architecture. The proposed approach resulted in an absolute increase of about 2% in the performance of the proposed CNN. The results demonstrate the potential of transfer learning in the field of medical image analysis, indicate the textural nature of the problem and show that the method used for training a network can be as important as designing its architecture.

ACS Style

Stergios Christodoulidis; Marios Anthimopoulos; Lukas Ebner; Andreas Christe; Stavroula Mougiakakou. Multisource Transfer Learning With Convolutional Neural Networks for Lung Pattern Analysis. IEEE Journal of Biomedical and Health Informatics 2016, 21, 76 -84.

AMA Style

Stergios Christodoulidis, Marios Anthimopoulos, Lukas Ebner, Andreas Christe, Stavroula Mougiakakou. Multisource Transfer Learning With Convolutional Neural Networks for Lung Pattern Analysis. IEEE Journal of Biomedical and Health Informatics. 2016; 21 (1):76-84.

Chicago/Turabian Style

Stergios Christodoulidis; Marios Anthimopoulos; Lukas Ebner; Andreas Christe; Stavroula Mougiakakou. 2016. "Multisource Transfer Learning With Convolutional Neural Networks for Lung Pattern Analysis." IEEE Journal of Biomedical and Health Informatics 21, no. 1: 76-84.

Journal article
Published: 29 February 2016 in IEEE Transactions on Medical Imaging
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Automated tissue characterization is one of the most crucial components of a computer aided diagnosis (CAD) system for interstitial lung diseases (ILDs). Although much research has been conducted in this field, the problem remains challenging. Deep learning techniques have recently achieved impressive results in a variety of computer vision problems, raising expectations that they might be applied in other domains, such as medical image analysis. In this paper, we propose and evaluate a convolutional neural network (CNN), designed for the classification of ILD patterns. The proposed network consists of 5 convolutional layers with 2 × 2 kernels and LeakyReLU activations, followed by average pooling with size equal to the size of the final feature maps and three dense layers. The last dense layer has 7 outputs, equivalent to the classes considered: healthy, ground glass opacity (GGO), micronodules, consolidation, reticulation, honeycombing and a combination of GGO/reticulation. To train and evaluate the CNN, we used a dataset of 14696 image patches, derived by 120 CT scans from different scanners and hospitals. To the best of our knowledge, this is the first deep CNN designed for the specific problem. A comparative analysis proved the effectiveness of the proposed CNN against previous methods in a challenging dataset. The classification performance ( ~ 85.5%) demonstrated the potential of CNNs in analyzing lung patterns. Future work includes, extending the CNN to three-dimensional data provided by CT volume scans and integrating the proposed method into a CAD system that aims to provide differential diagnosis for ILDs as a supportive tool for radiologists.

ACS Style

Marios Anthimopoulos; Stergios Christodoulidis; Lukas Ebner; Andreas Christe; Stavroula Mougiakakou. Lung Pattern Classification for Interstitial Lung Diseases Using a Deep Convolutional Neural Network. IEEE Transactions on Medical Imaging 2016, 35, 1207 -1216.

AMA Style

Marios Anthimopoulos, Stergios Christodoulidis, Lukas Ebner, Andreas Christe, Stavroula Mougiakakou. Lung Pattern Classification for Interstitial Lung Diseases Using a Deep Convolutional Neural Network. IEEE Transactions on Medical Imaging. 2016; 35 (5):1207-1216.

Chicago/Turabian Style

Marios Anthimopoulos; Stergios Christodoulidis; Lukas Ebner; Andreas Christe; Stavroula Mougiakakou. 2016. "Lung Pattern Classification for Interstitial Lung Diseases Using a Deep Convolutional Neural Network." IEEE Transactions on Medical Imaging 35, no. 5: 1207-1216.

Conference paper
Published: 21 August 2015 in Transactions on Petri Nets and Other Models of Concurrency XV
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Diet management is a key factor for the prevention and treatment of diet-related chronic diseases. Computer vision systems aim to provide automated food intake assessment using meal images. We propose a method for the recognition of already segmented food items in meal images. The method uses a 6-layer deep convolutional neural network to classify food image patches. For each food item, overlapping patches are extracted and classified and the class with the majority of votes is assigned to it. Experiments on a manually annotated dataset with 573 food items justified the choice of the involved components and proved the effectiveness of the proposed system yielding an overall accuracy of 84.9%.

ACS Style

Stergios Christodoulidis; Marios Anthimopoulos; Stavroula G. Mougiakakou. Food Recognition for Dietary Assessment Using Deep Convolutional Neural Networks. Transactions on Petri Nets and Other Models of Concurrency XV 2015, 458 -465.

AMA Style

Stergios Christodoulidis, Marios Anthimopoulos, Stavroula G. Mougiakakou. Food Recognition for Dietary Assessment Using Deep Convolutional Neural Networks. Transactions on Petri Nets and Other Models of Concurrency XV. 2015; ():458-465.

Chicago/Turabian Style

Stergios Christodoulidis; Marios Anthimopoulos; Stavroula G. Mougiakakou. 2015. "Food Recognition for Dietary Assessment Using Deep Convolutional Neural Networks." Transactions on Petri Nets and Other Models of Concurrency XV , no. : 458-465.

Conference paper
Published: 01 August 2014 in 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society
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Over the last decade, a plethora of computer-aided diagnosis (CAD) systems have been proposed aiming to improve the accuracy of the physicians in the diagnosis of interstitial lung diseases (ILD). In this study, we propose a scheme for the classification of HRCT image patches with ILD abnormalities as a basic component towards the quantification of the various ILD patterns in the lung. The feature extraction method relies on local spectral analysis using a DCT-based filter bank. After convolving the image with the filter bank, q-quantiles are computed for describing the distribution of local frequencies that characterize image texture. Then, the gray-level histogram values of the original image are added forming the final feature vector. The classification of the already described patches is done by a random forest (RF) classifier. The experimental results prove the superior performance and efficiency of the proposed approach compared against the state-of-the-art. Author(s) Anthimopoulos, M. ARTORG Center for Biomed. Eng. Res., Univ. of Bern, Bern, Switzerland Christodoulidis, S. ; Christe, A. ; Mougiakakou, S.

ACS Style

M. Anthimopoulos; S. Christodoulidis; A. Christe; S. Mougiakakou; Anthimopoulos M.. Classification of interstitial lung disease patterns using local DCT features and random forest. 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society 2014, 2014, 6040 -6043.

AMA Style

M. Anthimopoulos, S. Christodoulidis, A. Christe, S. Mougiakakou, Anthimopoulos M.. Classification of interstitial lung disease patterns using local DCT features and random forest. 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. 2014; 2014 ():6040-6043.

Chicago/Turabian Style

M. Anthimopoulos; S. Christodoulidis; A. Christe; S. Mougiakakou; Anthimopoulos M.. 2014. "Classification of interstitial lung disease patterns using local DCT features and random forest." 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society 2014, no. : 6040-6043.