Over the past two decades, malignant melanoma incidence rate has dramatically risen but melanoma mortality has only recently stabilized. Due to its propensity to metastasize and lack of effective therapies for most patients with advanced disease, early detection of melanoma is a clinical imperative. Thickness is one of the most important factor in melanoma prognosis and it is used to establish the size of the surgical margin, as well as to select patients for sentinel lymph node biopsy. However, little work has concentrated on the evaluation of melanoma thickness both from the clinical as well as computer-aided diagnostic side. To address this problem, we propose an effective computer-vision based machine learning tool that can perform the preoperative evaluation of melanoma thickness. The novelty of our approach is that we directly predict the thickness of the skin lesion into one of three classes: less than 0.75 mm, 0.76-1.5 mm, and greater that 1.5 mm. In this study, we use transfer learning of the pre-trained, adapted to our application VGG-19 convolutional neural network (CNN) with an adjusted densely-connected classifier. Due to the limited data we investigate the transfer learning method where we apply knowledge from model trained on a different task. Our database contains 244 dermoscopy images. Experiments confirm the developed algorithm's ability to classify skin lesion thickness with 87.2% overall accuracy what is a state-of-the-art result in melanoma thickness prediction.

Tissue segmentation in whole-slide images is an important task in digital pathology, required for efficient and accurate computer-aided diagnostics. Precise tissue segmentation is particularly significant for a correct diagnosis in cases, when tissue structure of a specimen is very porous, such as skin specimens. In this paper, we addressed the problem of fore- and background segmentation in histopatological images of skin specimens stained with hematoxylin and eosin (H&E), which has not been solved yet, by a novel method based on a combination of statistical analysis, color thresholding, and binary morphology. We validated our algorithm on large extracts from 60 high-resolution whole slide images, with differing staining quality and captured under varying imaging conditions, from three laboratories. The size of extracts varies from 2000×1500 to 20000×30000 pixels and the number of images used in our study matches the number of H&E images used by other research teams. We compared our method to the published ones (GrabCut and FESI) and showed that our approach outperforms its counterparts (Jaccard index of 0.929 vs. 0.776 and 0.695).

In this research, we present a semi-supervised segmentation solution using convolutional autoencoders to solve the problem of segmentation tasks having a small number of ground-truth images. We evaluate the proposed deep network architecture for the detection of nests of nevus cells in histopathological images of skin specimens is an important step in dermatopathology. The diagnostic criteria based on the degree of uniformity and symmetry of border irregularities are particularly vital in dermatopathology, in order to distinguish between benign and malignant skin lesions. However, to the best of our knowledge, it is the first described method to segment the nests region. The novelty of our approach is not only the area of research, but, furthermore, we address a problem with a small ground-truth dataset. We propose an effective computer-vision based deep learning tool that can perform the nests segmentation based on an autoencoder architecture with two learning steps. Experimental results verified the effectiveness of the proposed approach and its ability to segment nests areas with Dice similarity coefficient 0.81, sensitivity 0.76, and specificity 0.94, which is a state-of-the-art result.

Background. Malignant melanoma is among the fastest increasing malignancies in many countries. With the help of new tools, such as teledermoscopy referrals between primary healthcare and dermatology clinics, the diagnosis of these patients could be made more efficient. The introduction of a high‐quality smartphone with a built‐in digital camera may make the early detection more convenient. This study presents novel directions for early detection of malignant melanoma based on a smartphone application. Objectives and Methods. In this study, we concentrate on a precise description of a complex infrastructure of a fully automated computer‐aided diagnostic system for early detection of malignant melanoma. The framework has been customized for a dermoscope that is customized to attach to the smartphone to be able to carry out mobile teledermoscopy. The application requirements, architecture, and computational methods as well as behavioral and dynamic aspects have been presented in this paper. Conclusion. This paper presents a broad application architecture, which can be easily customized for rapid deployment of a sophisticated health application. Mobile teledermoscopy is a new horizon that might become in the future the basis of the early detection of pigmented skin lesions as a screening tool for primary care doctors and inexperienced dermatologists.

Clostridioides difficile infection (CDI) is an enteric bacterial disease that is increasing in incidence worldwide. Symptoms of CDI range from mild diarrhea to severe life-threatening inflammation of the colon. While antibiotics are standard-of-care treatments for CDI, they are also the biggest risk factor for development of CDI and recurrence. Therefore, novel therapies that successfully treat CDI and protect against recurrence are an unmet clinical need. Screening for novel drug leads is often tested by manual image analysis. The process is slow, tedious and is subject to human error and bias. So far, little work has focused on computer-aided screening for drug leads based on fluorescence images. Here, we propose a novel method to identify characteristic morphological changes in human fibroblast cells exposed to C. difficile toxins based on computer vision algorithms supported by deep learning methods. Classical image processing algorithms for the pre-processing stage are used together with an adjusted pre-trained deep convolutional neural network responsible for cell classification. In this study, we take advantage of transfer learning methodology by examining pre-trained VGG-19, ResNet50, Xception, and DenseNet121 convolutional neural network (CNN) models with adjusted, densely connected classifiers. We compare the obtained results with those of other machine learning algorithms and also visualize and interpret them. The proposed models have been evaluated on a dataset containing 369 images with 6112 cases. DenseNet121 achieved the highest results with a 93.5% accuracy, 92% sensitivity, and 95% specificity, respectively.