Label refinement for noisy annotation in weakly supervised segmentation

Deep learning has revolutionized medical image analysis, promising to significantly improve the precision of diagnoses and therapies through advanced segmentation methods. However, the efficacy of deep neural networks is often compromised by the prevalence of imperfect medical labels, while acquiring large-scale, accurately labeled data remains a prohibitive challenge. To address the imperfect label issue, we introduce a novel learning framework that iteratively optimizes both a neural network and its label set to enhance segmentation accuracy. This framework operates through two steps: initially, it robustly trains on a dataset with label noise, distinguishing between clean and noisy labels, and subsequently, it refines noisy labels based on high-confidence predictions from the robust network. By applying this method, not only is the network trained more effectively on imperfect data, but the dataset is progressively cleaned and expanded. Our evaluations are conducted on retina Optical Coherence Tomography datasets using U-Net and SegNet architectures, and demonstrate substantial improvements in segmentation accuracy and data quality, advancing the capabilities of weakly supervised segmentation in medical imaging.