Attention inverted feature perturbation for semi-supervised medical image segmentation

Accurate medical image segmentation is essential for reliable diagnosis, surgical planning, and disease monitoring. Semi-supervised medical image segmentation offers great potential by exploiting abundant unlabeled data with limited annotations, but it is prone to confirmation bias. To overcome this, we propose Attention Inverted Feature Perturbation (AIFP), a novel method that adaptively inverts feature-level attention weights to generate perturbations. This strategy encourages diversity and maintains independence between networks within a co-training framework, thereby mitigating confirmation bias. Extensive experiments on four public benchmarks validate the effectiveness of AIFP. Specifically, our method achieves Dice scores of 89.90% on ACDC and 91.01% on LA using only 10% labeled data, and 84.58% on Pancreas-NIH and 82.58% on PROMISE12 using 20% labeled data. These results consistently outperform state-of-the-art semi-supervised approaches, highlighting the practical value of AIFP in advancing accurate and robust medical image segmentation. AIFP enables reliable segmentation with limited annotations, supporting critical tasks such as left atrium delineation, pancreas boundary identification, and prostate segmentation. By reducing annotation demands while ensuring robustness, it has the potential to accelerate the clinical adoption of artificial intelligence-driven imaging tools.