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A critical challenge of training deep learning models in the DigitalPathology (DP) domain is the high annotation cost by medical experts. One wayto tackle this issue is via transfer learning from the natural image domain(NI), where the annotation cost is considerably cheaper. Cross-domain transferlearning from NI to DP is shown to be successful via class labels. Onepotential weakness of relying on class labels is the lack of spatialinformation, which can be obtained from spatial labels such as full pixel-wisesegmentation labels and scribble labels. We demonstrate that scribble labelsfrom NI domain can boost the performance of DP models on two cancerclassification datasets (Patch Camelyon Breast Cancer and Colorectal Cancerdataset). Furthermore, we show that models trained with scribble labels yieldthe same performance boost as full pixel-wise segmentation labels despite beingsignificantly easier and faster to collect.

Learning with Less Labels in Digital Pathology via Scribble Supervision from Natural Images