Research map / Label-efficient and robust learning / Semi-supervised learning
BoostMIS: Boosting Medical Image Semi-Supervised Learning With Adaptive Pseudo Labeling and Informative Active Annotation
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| combine adaptive pseudo-labeling with informative active annotation | medical image SSL · MESCC two-grading, 30% labels | accuracy: 92.94$\pm$ 0.37 -> 95.82$\pm$ 0.28 | Helped |
| combine adaptive pseudo-labeling with informative active annotation | medical image SSL · MESCC six-grading, 30% labels | accuracy: 58.77$\pm$ 0.62 -> 61.47$\pm$ 0.56 | Helped |
| use BoostMIS instead of FixMatch | MESCC SSL · MESCC two-grading, unlabeled training data | correct pseudo-label ratio: 88.14 -> 92.45 | Helped |
| use BoostMIS instead of FixMatch | MESCC SSL · MESCC six-grading, unlabeled training data | correct pseudo-label ratio: 65.46 -> 73.63 | Helped |
| add an adaptive threshold instead of using a fixed threshold (ablation) | BoostMIS · MESCC two-grading, 20% AL-selected samples | accuracy: 94.98 -> 95.82 | Helped |
| add an adaptive threshold instead of using a fixed threshold (ablation) | BoostMIS · MESCC six-grading, 20% AL-selected samples | accuracy: 60.07 -> 61.47 | Helped |
| add balanced uncertainty selection (ablation) | BoostMIS · MESCC two-grading, 20% AL-selected samples | accuracy: 94.06 -> 95.82 | Helped |
| add balanced uncertainty selection (ablation) | BoostMIS · MESCC six-grading, 20% AL-selected samples | accuracy: 60.44 -> 61.47 | Helped |
| add adversarial unstability selection (ablation) | BoostMIS · MESCC two-grading, 20% AL-selected samples | accuracy: 92.57 -> 95.82 | Helped |
| add adversarial unstability selection (ablation) | BoostMIS · MESCC six-grading, 20% AL-selected samples | accuracy: 58.03 -> 61.47 | Helped |
| replace random AL annotation sampling with informative selection (ablation) | BoostMIS SSL+AL · MESCC two-grading, 20% AL-selected samples | accuracy: 91.45 -> 95.82 | Helped |
| replace random AL annotation sampling with informative selection (ablation) | BoostMIS SSL+AL · MESCC six-grading, 20% AL-selected samples | accuracy: 53.76 -> 61.47 | Helped |
Closest papers by meaning
- Systematic Comparison of Semi-supervised and Self-supervised Learning for Medical Image Classification (CVPR 2024 · similarity 0.86)
- InterLUDE: Interactions between Labeled and Unlabeled Data to Enhance Semi-Supervised Learning (ICML 2024 · similarity 0.84)
- Steady Progress Beats Stagnation: Mutual Aid of Foundation and Conventional Models in Mixed Domain Semi-Supervised Medical Image Segmentation (CVPR 2025 · similarity 0.84)
- Towards Realistic Semi-supervised Medical Image Classification (AAAI 2025 · similarity 0.84)
- Semi-supervised TEE Segmentation via Interacting with SAM Equipped with Noise-Resilient Prompting (AAAI 2024 · similarity 0.84)
- Towards Cost-Effective Learning: A Synergy of Semi-Supervised and Active Learning (CVPR 2025 · similarity 0.84)
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