Papers › Alternate Diverse Teaching for Semi-supervised Medical Image Segmentation
Alternate Diverse Teaching for Semi-supervised Medical Image Segmentation
Zhen Zhao, Zicheng Wang, Longyue Wang, Dian Yu, Yixuan Yuan, Luping Zhou
Semi-supervised medical image segmentation studies have shown promise in training models with limited labeled data. However, current dominant teacher-student based approaches can suffer from the confirmation bias. To address this challenge, we propose AD-MT, an alternate diverse teaching approach in a teacher-student framework. It involves a single student model and two non-trainable teacher models that are momentum-updated periodically and randomly in an alternate fashion. To mitigate the confirmation bias from the diverse supervision, the core of AD-MT lies in two proposed modules: the Random Periodic Alternate (RPA) Updating Module and the Conflict-Combating Module (CCM). The RPA schedules the alternating diverse updating process with complementary data batches, distinct data augmentation, and random switching periods to encourage diverse reasoning from different teaching perspectives. The CCM employs an entropy-based ensembling strategy to encourage the model to learn from both the consistent and conflicting predictions between the teachers. Experimental results demonstrate the effectiveness and superiority of our AD-MT on the 2D and 3D medical segmentation benchmarks across various semi-supervised settings.
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Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Semi-supervised Medical Image Segmentation | ACDC 5% labeled data | AD-MT | Dice (Average) | 88.75 | #1 of 4 | Archive leaderboard | report |
| Semi-supervised Medical Image Segmentation | LA 5% labeled data | AD-MT | Average Dice | 89.63 | #1 of 1 | Archive leaderboard | report |
| Semi-supervised Medical Image Segmentation | Pancreas-CT 10% labeled data | AD-MT | Dice (Average) | 80.21 | #1 of 1 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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