Learning with Less Labels in Digital Pathology via Scribble Supervision from Natural Images
This addresses the problem of expensive medical expert annotations for digital pathology models, offering a more efficient labeling method, though it is incremental as it builds on existing cross-domain transfer learning approaches.
The paper tackles the high annotation cost in digital pathology by using scribble supervision from natural images, showing that scribble labels boost performance on cancer classification datasets (Patch Camelyon Breast Cancer and Colorectal Cancer) and achieve the same performance as full pixel-wise segmentation labels while being easier to collect.
A critical challenge of training deep learning models in the Digital Pathology (DP) domain is the high annotation cost by medical experts. One way to tackle this issue is via transfer learning from the natural image domain (NI), where the annotation cost is considerably cheaper. Cross-domain transfer learning from NI to DP is shown to be successful via class labels. One potential weakness of relying on class labels is the lack of spatial information, which can be obtained from spatial labels such as full pixel-wise segmentation labels and scribble labels. We demonstrate that scribble labels from NI domain can boost the performance of DP models on two cancer classification datasets (Patch Camelyon Breast Cancer and Colorectal Cancer dataset). Furthermore, we show that models trained with scribble labels yield the same performance boost as full pixel-wise segmentation labels despite being significantly easier and faster to collect.