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In certain types of cancerous tissue, mitotic count has been shown to beassociated with tumor proliferation, poor prognosis, and therapeuticresistance. Due to the high inter-rater variability of mitotic counting bypathologists, convolutional neural networks (CNNs) have been employed to reducethe subjectivity of mitosis detection in hematoxylin and eosin (H&E)-stainedwhole slide images. However, most existing models have performance that lagsbehind expert panel review and only incorporate visual information. In thiswork, we demonstrate that pre-trained large-scale vision-language models thatleverage both visual features and natural language improve mitosis detectionaccuracy. We formulate the mitosis detection task as an image captioning taskand a visual question answering (VQA) task by including metadata such as tumorand scanner types as context. The effectiveness of our pipeline is demonstratedvia comparison with various baseline models using 9,501 mitotic figures and11,051 hard negatives (non-mitotic figures that are difficult to characterize)from the publicly available Mitosis Domain Generalization Challenge (MIDOG22)dataset.

Improving mitosis detection on histopathology images using large vision-language models