Learning with less data via Weakly Labeled Patch Classification in Digital Pathology
This addresses data scarcity for medical imaging applications, but it is incremental as it builds on existing weakly supervised methods.
The paper tackles the problem of scarce labeled data in digital pathology by learning transferable features from weakly labeled data, achieving competitive patch classification results on colorectal cancer and PatchCamelyon datasets while using an order of magnitude less labeled data.
In Digital Pathology (DP), labeled data is generally very scarce due to the requirement that medical experts provide annotations. We address this issue by learning transferable features from weakly labeled data, which are collected from various parts of the body and are organized by non-medical experts. In this paper, we show that features learned from such weakly labeled datasets are indeed transferable and allow us to achieve highly competitive patch classification results on the colorectal cancer (CRC) dataset [1] and the PatchCamelyon (PCam) dataset [2] while using an order of magnitude less labeled data.