Uncertainty Awareness Enables Efficient Labeling for Cancer Subtyping in Digital Pathology

Nirhoshan Sivaroopan, Chamuditha Jayanga Galappaththige, Chalani Ekanayake, Hasindri Watawana, Ranga Rodrigo, Chamira U.S. Edussooriya, Dushan N. Wadduwage; Proceedings of the Winter Conference on Applications of Computer Vision (WACV), 2025, pp. 589-598

Machine-learning-assisted cancer subtyping is a promising avenue in digital pathology. Cancer subtyping models however require careful training using expert annotations so that they can be inferred with a degree of known certainty (or uncertainty). To this end we introduce the concept of uncertainty awareness into a self-supervised contrastive learning model. This is achieved by computing an evidence vector at every epoch which assesses the model's confidence in its predictions. The derived uncertainty score is then utilized as a metric to selectively label the most crucial images that require further annotation thus iteratively refining the training process. With just 1-10% of strategically selected annotations we attain state-of-the-art performance in cancer subtyping on benchmark datasets. Our method not only strategically guides the annotation process to minimize the need for extensive labeled datasets but also improve the precision and efficiency of classifications. This development is particularly beneficial in settings where the availability of labeled data is limited offering a promising direction for future research and application in digital pathology. Our code is available at https://github.com/Nirhoshan/AI-for-histopathology