Swin transformers are robust to distribution and concept drift in endoscopy-based longitudinal rectal cancer assessment

TL;DR

This study demonstrates that Swin transformers are highly robust to distribution and concept drift in endoscopy images, enabling consistent longitudinal rectal cancer assessment across varied imaging conditions.

Contribution

The paper introduces a hierarchical Swin transformer model that outperforms other deep learning models in detecting rectal cancer and regrowth in endoscopy images, even under distribution shifts.

Findings

Swin transformer achieved high accuracy in in-distribution and out-of-distribution datasets.

Swin outperformed ResNet and ViT models in robustness to color shifts.

Deep learning models can provide objective, consistent assessment in endoscopic cancer monitoring.

Abstract

Endoscopic images are used at various stages of rectal cancer treatment starting from cancer screening, diagnosis, during treatment to assess response and toxicity from treatments such as colitis, and at follow up to detect new tumor or local regrowth (LR). However, subjective assessment is highly variable and can underestimate the degree of response in some patients, subjecting them to unnecessary surgery, or overestimate response that places patients at risk of disease spread. Advances in deep learning has shown the ability to produce consistent and objective response assessment for endoscopic images. However, methods for detecting cancers, regrowth, and monitoring response during the entire course of patient treatment and follow-up are lacking. This is because, automated diagnosis and rectal cancer response assessment requires methods that are robust to inherent imaging illumination variations and confounding conditions (blood, scope, blurring) present in endoscopy images as well as changes to the normal lumen and tumor during treatment. Hence, a hierarchical shifted window (Swin) transformer was trained to distinguish rectal cancer from normal lumen using endoscopy images. Swin as well as two convolutional (ResNet-50, WideResNet-50), and vision transformer (ViT) models were trained and evaluated on follow-up longitudinal images to detect LR on private dataset as well as on out-of-distribution (OOD) public colonoscopy datasets to detect pre/non-cancerous polyps. Color shifts were applied using optimal transport to simulate distribution shifts. Swin and ResNet models were similarly accurate in the in-distribution dataset. Swin was more accurate than other methods (follow-up: 0.84, OOD: 0.83) even when subject to color shifts (follow-up: 0.83, OOD: 0.87), indicating capability to provide robust performance for longitudinal cancer assessment.