J Vince Pulido, Shan Guleria, Lubaina Ehsan, Matthew Fasullo, Robert Lippman, Pritesh Mutha, Tilak Shah, Sana Syed, Donald E Brown
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Semi-Supervised Classification of Noisy, Gigapixel Histology Images.
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.