Dual Teacher with Dempster-Shafer Guidance for Decision Making in Semi-Supervised Small Object Detection

摘要

Small-scale object detection remains a major challenge in semi-supervised object detection (SSOD), particularly in medical image analysis. Conventional teacher models often struggle to accurately capture the features of low-contrast small lesions, leading to noisy pseudo-labels in both localization and classification, which introduces severe uncertainty and degrades detection performance. To address this issue, we propose Dual Teacher, a novel multimodal semi-supervised detection framework designed to enhance pseudo-label reliability and improve small-scale lesion detection. Specifically, we introduce two complementary teacher models: Hybrid-Scale Teacher, which exploits downsampled views to strengthen multi-scale feature learning, and Entropy-Based Multi-Modal Teacher, which leverages entropy maps to refine the quality of small-scale pseudo-labels. To effectively fuse predictions from both teachers and resolve conflicts, we propose a Dempster-Shafer-based Dual-Teacher pseudo-label fusion strategy that explicitly models uncertainty and optimizes classification confidence. Additionally, we introduce a class-adaptive threshold mechanism that dynamically adjusts pseudo-label selection based on dual-teacher predictions, further boosting the recall of small-scale lesions. Extensive experiments on the Dental Disease Dataset, ChestX-Det and M3FD demonstrate that our method consistently surpasses state-of-the-art SSOD approaches. Code is available at: https://github.com/z316910/Dual-Teacher.git.