Semi-Supervised Classification of Noisy, Gigapixel Histology Images
J. Vince Pulido, Shan Guleria, Lubaina Ehsan, Matthew J. Fasullo, Robert H. Lippman, Pritesh R. Mutha, Tilak Shah, Sana Syed, Donald E. Brown · 2020
One of the greatest obstacles in the adoption of deep neural networks for new medical applications is that training these models typically require a large amount of manually labeled training samples. In this body of work, we investigate the semi-supervised scenario where one has access to large amounts of unlabeled data and only a few labeled samples. We study the performance of MixMatch and FixMatch-two popular semi-supervised learning methods-on a histology dataset. More specifically, we study these models' impact under a highly noisy and imbalanced setting. The findings here motivate the development of semi-supervised methods to ameliorate problems commonly encountered in medical data applications.