Polyp-E: Benchmarking the Robustness of Deep Segmentation Models via Polyp Editing

TL;DR

This paper introduces Polyp-E, a realistic synthetic dataset created using attribute editing of polyps via Latent Diffusion Models, to benchmark and improve the robustness of deep segmentation models in colonoscopy analysis.

Contribution

The paper presents a new synthetic dataset, Polyp-E, generated through attribute editing, and demonstrates its effectiveness in benchmarking and enhancing segmentation model robustness.

Findings

Most segmentation models are sensitive to attribute variations.

Attribute editing improves model generalization in both in-distribution and out-of-distribution scenarios.

Polyp-E dataset is highly realistic and useful for benchmarking.

Abstract

Automatic polyp segmentation is helpful to assist clinical diagnosis and treatment. In daily clinical practice, clinicians exhibit robustness in identifying polyps with both location and size variations. It is uncertain if deep segmentation models can achieve comparable robustness in automated colonoscopic analysis. To benchmark the model robustness, we focus on evaluating the robustness of segmentation models on the polyps with various attributes (e.g. location and size) and healthy samples. Based on the Latent Diffusion Model, we perform attribute editing on real polyps and build a new dataset named Polyp-E. Our synthetic dataset boasts exceptional realism, to the extent that clinical experts find it challenging to discern them from real data. We evaluate several existing polyp segmentation models on the proposed benchmark. The results reveal most of the models are highly sensitive to attribute variations. As a novel data augmentation technique, the proposed editing pipeline can improve both in-distribution and out-of-distribution generalization ability. The code and datasets will be released.