Classifying Cervical Histopathological Whole Slide Images via Deep Multi-Instance Transfer Learning

The cervical histopathology analysis result is the gold standard for cervical cancer diagnosis. Conventional histopathological examination depends on pathologists' observation under microscope, which is notoriously labor-intensive and subjective. The popularization of digital pathology technology makes the collection of the cervical histopathological whole slide images (WSIs) more convenient, so it has become possible to develop computer-aided diagnosis methods for cervical cancer. In this work, we first collected the cervical histopathological WSIs from 917 patients with pathological diagnosis through a retrospective study, of which 286 WSIs contained annotations of several lesion areas that were manually outlined by the pathologists. Then we proposed a method for classifying cervical histopathological WSIs by combining deep multi-instance transfer learning (DMITL) and support vector machine (SVM). The DMITL aimed for learning the representations of the WSIs, and the SVM was used for building the classification model of the WSIs. We generated the training and test sets based on our collected WSIs to train and evaluate our method. The validation results have shown that the good performance of our proposed method.