Transfer Learning From Nucleus Detection To Classification In Histopathology Images

Yousefi, S.; Nie, Y.

doi:10.1101/530113

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bioRxiv · 10.1101/530113

Transfer Learning From Nucleus Detection To Classification In Histopathology Images

Yousefi, S.·Nie, Y.

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Abstract

Despite significant recent success, modern computer vision techniques such as Convolutional Neural Networks (CNNs) are expensive to apply to cell-level prediction problems in histopathology images due to difficulties in providing cell-level supervision. This work explores the transferability of features learned by an object detection CNN (Faster R-CNN) to nucleus classification in histopathology images. We detect nuclei in these images using class-agnostic models trained on small annotated patches, and use the CNN representations of detected nuclei to cluster and classify them. We show that with a small training dataset, the proposed pipeline can achieve superior nucleus detection and classification performance, and generalizes well to unseen stain types.

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Yousefi, S., Nie, Y.. 2019-01-24. Transfer Learning From Nucleus Detection To Classification In Histopathology Images. https://doi.org/10.1101/530113

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