Pixel-level Counterfactual Contrastive Learning for Medical Image Segmentation
This work addresses the need for robust segmentation in medical imaging with limited annotations, though it is incremental as it builds on existing contrastive and counterfactual methods.
The paper tackles the problem of expensive manual annotations for medical image segmentation by proposing a pipeline that combines counterfactual generation with dense contrastive learning, achieving ~94% Dice Similarity Coefficient (DSC) on challenging data.
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.