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