RACR-MIL: Weakly Supervised Skin Cancer Grading using Rank-Aware Contextual Reasoning on Whole Slide Images

• URL: http://arxiv.org/abs/2308.15618v1
• Date: Tue, 29 Aug 2023 20:25:49 GMT
• Title: RACR-MIL: Weakly Supervised Skin Cancer Grading using Rank-Aware Contextual Reasoning on Whole Slide Images
• Authors: Anirudh Choudhary, Angelina Hwang, Jacob Kechter, Krishnakant Saboo, Blake Bordeaux, Puneet Bhullar, Nneka Comfere, David DiCaudo, Steven Nelson, Emma Johnson, Leah Swanson, Dennis Murphree, Aaron Mangold, Ravishankar K. Iyer
• Abstract summary: Cutaneous squamous cell cancer (cSCC) is the second most common skin cancer in the US. We propose an automated weakly-supervised grading approach for c SCC WSIs. The proposed model transforms each WSI into a bag of tiled patches and leverages attention-based multiple-instance learning to assign a WSI-level grade.
• Score: 1.7625232415232948
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Cutaneous squamous cell cancer (cSCC) is the second most common skin cancer in the US. It is diagnosed by manual multi-class tumor grading using a tissue whole slide image (WSI), which is subjective and suffers from inter-pathologist variability. We propose an automated weakly-supervised grading approach for cSCC WSIs that is trained using WSI-level grade and does not require fine-grained tumor annotations. The proposed model, RACR-MIL, transforms each WSI into a bag of tiled patches and leverages attention-based multiple-instance learning to assign a WSI-level grade. We propose three key innovations to address general as well as cSCC-specific challenges in tumor grading. First, we leverage spatial and semantic proximity to define a WSI graph that encodes both local and non-local dependencies between tumor regions and leverage graph attention convolution to derive contextual patch features. Second, we introduce a novel ordinal ranking constraint on the patch attention network to ensure that higher-grade tumor regions are assigned higher attention. Third, we use tumor depth as an auxiliary task to improve grade classification in a multitask learning framework. RACR-MIL achieves 2-9% improvement in grade classification over existing weakly-supervised approaches on a dataset of 718 cSCC tissue images and localizes the tumor better. The model achieves 5-20% higher accuracy in difficult-to-classify high-risk grade classes and is robust to class imbalance.

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