Pixel-level Counterfactual Contrastive Learning for Medical Image Segmentation

TL;DR

This paper introduces a novel pixel-level contrastive learning framework for medical image segmentation that leverages counterfactual generation and silver-standard labels, significantly improving segmentation accuracy and robustness.

Contribution

It proposes a new dense contrastive learning pipeline with counterfactuals and supervised variants utilizing silver-standard labels for improved medical image segmentation.

Findings

Outperforms existing dense contrastive learning methods.

Supervised variants with silver-standard labels achieve ~94% DSC.

Enhances robustness to acquisition and pathological variations.

Abstract

Image segmentation relies on large annotated datasets, which are expensive and slow to produce. Silver-standard (AI-generated) labels are easier to obtain, but they risk introducing bias. Self-supervised learning, needing only images, has become key for pre-training. Recent work combining contrastive learning with counterfactual generation improves representation learning for classification but does not readily extend to pixel-level tasks. We propose a pipeline combining counterfactual generation with dense contrastive learning via Dual-View (DVD-CL) and Multi-View (MVD-CL) methods, along with supervised variants that utilize available silver-standard annotations. A new visualisation algorithm, the Color-coded High Resolution Overlay map (CHRO-map) is also introduced. Experiments show annotation-free DVD-CL outperforms other dense contrastive learning methods, while supervised variants using silver-standard labels outperform training on the silver-standard labeled data directly, achieving $\sim$94% DSC on challenging data. These results highlight that pixel-level contrastive learning, enhanced by counterfactuals and silver-standard annotations, improves robustness to acquisition and pathological variations.