Breast cancer is a serious threat to women. Many machine learning-based computer-aided diagnosis (CAD) methods have been proposed for the early diagnosis of breast cancer based on histopathological images. Even though many such classification methods achieved high accuracy, many of them lack the explanation of the classification process. In this paper, we compare the performance of conventional machine learning (CML) against deep learning (DL)-based methods. We also provide a visual interpretation for the task of classifying breast cancer in histopathological images. For CML-based methods, we extract a set of handcrafted features using three feature extractors and fuse them to get image representation that would act as an input to train five classical classifiers. For DL-based methods, we adopt the transfer learning approach to the well-known VGG-19 deep learning architecture, where its pre-trained version on the large scale ImageNet, is block-wise fine-tuned on histopathological images. The evaluation of the proposed methods is carried out on the publicly available BreaKHis dataset for the magnification dependent classification of benign and malignant breast cancer and their eight sub-classes, and a further validation on KIMIA Path960, a magnification-free histopathological dataset with 20 image classes, is also performed. After providing the classification results of CML and DL methods, and to better explain the difference in the classification performance, we visualize the learned features. For the DL-based method, we intuitively visualize the areas of interest of the best fine-tuned deep neural networks using attention maps to explain the decision-making process and improve the clinical interpretability of the proposed models. The visual explanation can inherently improve the pathologist’s trust in automated DL methods as a credible and trustworthy support tool for breast cancer diagnosis. The achieved results show that DL methods outperform CML approaches where we reached an accuracy between 94.05% and 98.13% for the binary classification and between 76.77% and 88.95% for the eight-class classification, while for DL approaches, the accuracies range from 85.65% to 89.32% for the binary classification and from 63.55% to 69.69% for the eight-class classification.

Mammography remains the most prevalent imaging tool for early breast cancer screening. The language used to describe abnormalities in mammographic reports is based on the Breast Imaging Reporting and Data System (BI‐RADS). Assigning a correct BI‐RADS category to each examined mammogram is a strenuous and challenging task for even experts. This paper proposes a new and effective computer‐aided diagnosis (CAD) system to classify mammographic masses into four assessment categories in BI‐RADS. The mass regions are first enhanced by means of histogram equalization and then semiautomatically segmented based on the region growing technique. A total of 130 handcrafted BI‐RADS features are then extracted from the shape, margin, and density of each mass, together with the mass size and the patient’s age, as mentioned in BI‐RADS mammography. Then, a modified feature selection method based on the genetic algorithm (GA) is proposed to select the most clinically significant BI‐RADS features. Finally, a back‐propagation neural network (BPN) is employed for classification, and its accuracy is used as the fitness in GA. A set of 500 mammogram images from the digital database for screening mammography (DDSM) is used for evaluation. Our system achieves classification accuracy, positive predictive value, negative predictive value, and Matthews correlation coefficient of 84.5%, 84.4%, 94.8%, and 79.3%, respectively. To our best knowledge, this is the best current result for BI‐RADS classification of breast masses in mammography, which makes the proposed system promising to support radiologists for deciding proper patient management based on the automatically assigned BI‐RADS categories.

Partial hepatectomy is the first option for intrahepatic mass-forming cholangiocarcinoma (IMCC) treatment, which would prolong survival. The main reason for the poor outcome after curative resection is the high incidence of early recurrence (ER). The aim of this study was to investigate the combined predictive performance of qualitative and quantitative magnetic resonance imaging (MRI) features and prognostic immunohistochemical markers for the ER of IMCC. Forty-seven patients with pathologically proven IMCC were enrolled in this retrospective study. Preoperative contrast-enhanced MRI and post-operative immunohistochemical staining of epidermal growth factor receptors (EGFR), vascular endothelial growth factor receptor (VEGFR), P53 and Ki67 were performed. Univariate analysis identified clinic-radiologic and pathological risk factors of ER. Radiomics analysis was performed based on four MRI sequences including fat suppression T2-weighted imaging (T2WI/FS), arterial phase (AP), portal venous phase (PVP), and delayed phase (DP) contrast enhanced imaging. A clinicoradiologic-pathological (CRP) model, radiomics model, and combined model were developed. And ROC curves were used to explore their predictive performance for ER stratification. Enhancement patterns and VEGFR showed significant differences between the ER group and non-ER group (P = 0.001 and 0.034, respectively). The radiomics model based on AP, PVP and DP images presented superior AUC (0.889, 95% confidence interval (CI): 0.783–0.996) among seven radiomics models with a sensitivity of 0.938 and specificity of 0.839. The combined model, containing enhancement patterns, VEGFR and radiomics features, showed a preferable ER predictive performance compared to the radiomics model or CRP model alone, with AUC, sensitivity and specificity of 0.949, 0.875 and 0.774, respectively. The combined model was the superior predictive model of ER. Combining qualitative and quantitative MRI features and VEGFR enables ER prediction, thus facilitating personalized treatment for patients with IMCC.

Application of artificial intelligence in preoperative imaging of hepatocellular carcinoma: Current status and future perspectives