DUGS-MT: A Competitive Dual-Stream Framework With Uncertainty-Gated Consensus for Semi-Supervised Radiographic Segmentation

Abstract

Clinical workflows require segmentation of the ribs in chest radiographs (X-rays), but expert-annotated data is scarce, and semi-supervised methods are prone to confirmation bias, limiting the efficacy of automated diagnostics. This paper proposes the Dual Uncertainty-Gated Switch Mean Teacher (DUGS-MT), a dual-stream semi-supervised framework that addresses semi-supervised rib segmentation issues and promotes computer-aided diagnosis in Sustainable Development Goal 3 (SDG 3). An Uncertainty-Gated Switch mechanism with intra-consistency and cross-consistency filters out unreliable pseudo-labels and prevents errors from spreading across streams by limiting each student to learning from the other teacher's predictions when pixels have high confidence and low entropy. The model was evaluated on the VinDr-RibCXR public dataset using labelled data subsets ranging from 5% to 100% to assess its anatomical segmentation performance. With only 5% labelled data, the framework achieved a Dice Similarity Coefficient (DSC) of 0.8442 and a 95th Percentile Hausdorff Distance (HD95) of 6.7034, outperforming the fully supervised baseline (DSC 0.7971). Dual consistency losses act as a strong regularizer in the fully supervised setting, reaching a dataset record DSC of 0.8861 and HD95 of 3.5780. DUGS-MT outperformed fully and semi-supervised baselines and can segment data effectively even with few labels, indicating its potential for scalable, accessible clinical use.