On Transferability of Histological Tissue Labels in Computational Pathology

Mahdi S. Hosseini, Lyndon Chan, Weimin Huang, Yichen Wang, Danial Hasan, Corwyn Rowsell, Savvas Damaskinos, Konstantinos N. Plataniotis ;

Deep learning tools in computational pathology, unlike natural vision tasks, face with limited histological tissue labels for classification. This is due to expensive procedure of annotation done by expert pathologist. As a result, the current models are limited to particular diagnostic task in mind where the training workflow is repeated for different organ sites and diseases. In this paper, we explore the possibility of transferring diagnostically-relevant histology labels from a source-domain into multiple target-domains to classify similar tissue structures and cancer grades. We achieve this by training a Convolutional Neural Network (CNN) model on a source-domain of diverse histological tissue labels for classification and then transfer them to different target domains for diagnosis without re-training/fine-tuning (zero-shot). We expedite this by an efficient color augmentation to account for color disparity across different tissue scans and conduct thorough experiments for evaluation."