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ProsMAE

model1 events
papersSEP 11 04:00 UTC

ProsMAE: Multi-Source MAE Pretraining for Prostate Cancer ISUP Grade Classification

A new arXiv paper introduces ProsMAE, a masked autoencoder pretraining approach that draws on multiple data sources to classify ISUP grades from whole slide images. The authors address common obstacles in computational pathology, including gigapixel image sizes, staining and scanner variability, tissue artifacts, and scarce expert annotations. The work is a replacement submission on arXiv's machine learning category.

medical-imaging/self-supervised/benchmark/research