Cancer cell segmentation for cellularity prediction via a weakly labeled/strongly labeled hybrid convolutional neural network

David R. Chambers; Bradley B. Brimhall; Donald R. Poole Jr.; Edward A. Medina

doi:10.1117/12.2611636

4 April 2022 Cancer cell segmentation for cellularity prediction via a weakly labeled/strongly labeled hybrid convolutional neural network

David R. Chambers, Bradley B. Brimhall, Donald R. Poole Jr., Edward A. Medina

Author Affiliations +

Proceedings Volume 12039, Medical Imaging 2022: Digital and Computational Pathology; 120390A (2022) https://doi.org/10.1117/12.2611636
Event: SPIE Medical Imaging, 2022, San Diego, California, United States

Abstract

In our work, we present an approach to regressing breast cancer cellularity in patches extracted from Whole Slide Imagery (WSI) on Hematoxylin and Eosin (H&E) stains using a fully-convolutional neural network which is trained with two heads: one which computes a global average pool for weakly-labeled data (data with a cellularity score of 0- 1.0) and another which enforces pixel-wise activations for strongly-labeled (segmentation) data. Our method was the top-performing algorithm of all submissions to the BreastPathQ challenge, achieving a prediction probability of 0.941.

Conference Presentation

Citation Download Citation

David R. Chambers, Bradley B. Brimhall, Donald R. Poole Jr., and Edward A. Medina "Cancer cell segmentation for cellularity prediction via a weakly labeled/strongly labeled hybrid convolutional neural network", Proc. SPIE 12039, Medical Imaging 2022: Digital and Computational Pathology, 120390A (4 April 2022); https://doi.org/10.1117/12.2611636

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