Obtaining a large amount of labeled data in medical imaging is laborious and time-consuming, especially for histopathology. However, it is much easier and cheaper to get unlabeled data from whole-slide images (WSIs). Semi-supervised learning (SSL) is an effective way to utilize unlabeled data and alleviate the need for labeled data. For this reason, we proposed a framework that employs an SSL method to accurately detect cancerous regions with a novel annotation method called Minimal Point-Based annotation, and then utilize the predicted results with an innovative hybrid loss to train a classification model for subtyping. The annotator only needs to mark a few points and label them are cancer or not in each WSI. Experiments on three significant subtypes of renal cell carcinoma (RCC) proved that the performance of the classifier trained with the Min-Point annotated dataset is comparable to a classifier trained with the segmentation annotated dataset for cancer region detection. And the subtyping model outperforms a model trained with only diagnostic labels by 12% in terms of f1-score for testing WSIs.
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