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Deep learning for histopathology has been successfully used for diseaseclassification, image segmentation and more. However, combining image and textmodalities using current state-of-the-art methods has been a challenge due tothe high resolution of histopathology images. Automatic report generation forhistopathology images is one such challenge. In this work, we show that usingan existing pre-trained Vision Transformer in a two-step process of first usingit to encode 4096x4096 sized patches of the Whole Slide Image (WSI) and thenusing it as the encoder and an LSTM decoder for report generation, we can builda fairly performant and portable report generation mechanism that takes intoaccount the whole of the high resolution image, instead of just the patches. Weare also able to use representations from an existing powerful pre-trainedhierarchical vision transformer and show its usefulness in not just zero shotclassification but also for report generation.

Automatic Report Generation for Histopathology images using pre-trained Vision Transformers