  Objective: We develop a computer-aided diagnosis (CAD) system using deep
learning approaches for lesion detection and classification on whole-slide
images (WSIs) with breast cancer. The deep features being distinguishing in
classification from the convolutional neural networks (CNN) are demonstrated in
this study to provide comprehensive interpretability for the proposed CAD
system using pathological knowledge. Methods: In the experiment, a total of 186
slides of WSIs were collected and classified into three categories:
Non-Carcinoma, Ductal Carcinoma in Situ (DCIS), and Invasive Ductal Carcinoma
(IDC). Instead of conducting pixel-wise classification into three classes
directly, we designed a hierarchical framework with the multi-view scheme that
performs lesion detection for region proposal at higher magnification first and
then conducts lesion classification at lower magnification for each detected
lesion. Results: The slide-level accuracy rate for three-category
classification reaches 90.8% (99/109) through 5-fold cross-validation and
achieves 94.8% (73/77) on the testing set. The experimental results show that
the morphological characteristics and co-occurrence properties learned by the
deep learning models for lesion classification are accordant with the clinical
rules in diagnosis. Conclusion: The pathological interpretability of the deep
features not only enhances the reliability of the proposed CAD system to gain
acceptance from medical specialists, but also facilitates the development of
deep learning frameworks for various tasks in pathology. Significance: This
paper presents a CAD system for pathological image analysis, which fills the
clinical requirements and can be accepted by medical specialists with providing
its interpretability from the pathological perspective.
