Toward More Transparent and Accurate Cancer Diagnosis With an Unsupervised CAE Approach

Abstract

According to the Global Cancer Observatory, 2020, breast cancer is the most prevalent cancer type in both genders (11.7%), while prostate cancer is the second most common cancer type in men (14.1%). In digital pathology, Content-Based Medical Image Retrieval (CBMIR) is a powerful tool for improving cancer diagnosis by searching for similar histopathological Whole Slide Images (WSIs). CBMIR empowers pathologists to explore similar patches to their query, enhancing diagnostic reliability and accuracy. In this paper, a customized unsupervised Convolutional Auto Encoder (CAE) was developed in the proposed Unsupervised CBMIR (UCBMIR) to replicate the traditional cancer diagnosis workflow, offering the potential to enhance diagnostic accuracy and efficiency by reducing pathologists’ workload. Furthermore, it provides a more transparent supporting tool for pathologists in cancer diagnosis. UCBMIR was evaluated using two widely used numerical techniques in CBMIR, visual techniques, and compared with a classifier. Validation encompassed three data sets, including an external evaluation to demonstrate its effectiveness. UCBMIR achieved 99% and 80% top 5 recalls on BreaKHis and SICAPv2 with the first evaluation technique while using the second technique, it reached 91% and 70% precision for BreaKHis and SICAPv2, respectively. Moreover, UCBMIR displayed a strong capability to identify diverse patterns, yielding 81% accuracy in the top 5 predictions on an external image from Arvaniti. The proposed unsupervised CBMIR tool delivered 83% accuracy in retrieving images with the same cancer type.

Keywords

• Histopathological images
• content-based medical image retrieval (CBMIR)
• convolutional auto encoder
• unsupervised learning
• whole slide images (WSIs)
• digital pathology