Siamese Content-based Search Engine for a More Transparent Skin and Breast Cancer Diagnosis through Histological Imaging
This work addresses the need for more explainable computer-aided diagnosis tools for pathologists in histopathology, though it appears incremental as it builds on existing retrieval methods with specific dataset applications.
The paper tackles the problem of improving transparency in skin and breast cancer diagnosis by proposing Siamese network-based content-based histopathological image retrieval approaches, achieving a 70% F1 score for breast cancer and a 67% precision increase for skin cancer compared to existing methods.
Computer Aid Diagnosis (CAD) has developed digital pathology with Deep Learning (DL)-based tools to assist pathologists in decision-making. Content-Based Histopathological Image Retrieval (CBHIR) is a novel tool to seek highly correlated patches in terms of similarity in histopathological features. In this work, we proposed two CBHIR approaches on breast (Breast-twins) and skin cancer (Skin-twins) data sets for robust and accurate patch-level retrieval, integrating a custom-built Siamese network as a feature extractor. The proposed Siamese network is able to generalize for unseen images by focusing on the similar histopathological features of the input pairs. The proposed CBHIR approaches are evaluated on the Breast (public) and Skin (private) data sets with top K accuracy. Finding the optimum amount of K is challenging, but also, as much as K increases, the dissimilarity between the query and the returned images increases which might mislead the pathologists. To the best of the author's belief, this paper is tackling this issue for the first time on histopathological images by evaluating the top first retrieved images. The Breast-twins model achieves 70% of the F1score at the top first, which exceeds the other state-of-the-art methods at a higher amount of K such as 5 and 400. Skin-twins overpasses the recently proposed Convolutional Auto Encoder (CAE) by 67%, increasing the precision. Besides, the Skin-twins model tackles the challenges of Spitzoid Tumors of Uncertain Malignant Potential (STUMP) to assist pathologists with retrieving top K images and their corresponding labels. So, this approach can offer a more explainable CAD tool to pathologists in terms of transparency, trustworthiness, or reliability among other characteristics.