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Evaluating and interpreting caption prediction for histopathology images

The automatic generation of captions from medical images can provide for an efficient way to annotate histopathology images with natural language descriptions. Such large-scale annotation of medical images may help facilitate image retrieval tasks and standardize clinical ontologies. In this work, we focus on developing and methodically evaluating a new caption generation framework for histopathology whole-slide images. We introduce PathCap, a deep learning multi-scale framework, to predict captions from histopathology images using multi-scale views of whole-slide images. We demonstrate that our framework outperforms a standard baseline caption model on a diverse set of human tissues and provides interpretable contextual cues for understanding predicted captions. Finally, we draw attention to a novel dataset of histopathology images with captions from the Genotype-Tissue Expression (GTEx) project, providing a valuable dataset for the machine learning and healthcare community to benchmark future caption prediction and interpretation methods.

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