Predicting Cancer with a Recurrent Visual Attention Model for Histopathology Images
Automatically recognizing cancers from multi-gigapixel whole slide histopathology images is one of the challenges facing machine and deep learning based solutions for digital pathology. Currently, most automatic systems for histopathology are not scalable to large images and hence require a patch-based representation; a sub-optimal solution as it results in important additional computational costs but more importantly in the loss of contextual information. We present a novel attention-based model for predicting cancer from histopathology whole slide images. The proposed model is capable of attending to the most discriminative regions of an image by adaptively selecting a limited sequence of locations and only processing the selected areas of tissues. We demonstrate the utility of the proposed model on the slide-based prediction of macro and micro metastases in sentinel lymph nodes of breast cancer patients. We achieve competitive results with state-of-the-art convolutional networks while automatically identifying discriminative areas of tissues.
We thank NVIDIA Corporation for GPU donation and The Natural Sciences and Engineering Research Council of Canada (NSERC) for funding.
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