Research map / Detection and segmentation / Open-set and few-shot detection
AMOS: A Large-Scale Abdominal Multi-Organ Benchmark for Versatile Medical Image Segmentation
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Train UNet jointly on CT and MRI instead of training on each modality individually | UNet · AMOS-CT Val | mDice: 88.87 -> 89.42 (+0.55%) | Helped |
| Train UNet jointly on CT and MRI instead of training on each modality individually | UNet · AMOS-MRI Val | mDice: 85.59 -> 87.73 (+2.14%) | Helped |
| Pretrain on AMOS before fine-tuning | UNet · MSD-Liver | mDice: 80.31 -> 81.97 | Helped |
| Pretrain on AMOS before fine-tuning | UNet · MSD-Spleen | mDice: 95.39 -> 96.40 | Helped |
| Pretrain on AMOS before fine-tuning | UNet · MSD-Pancreas | mDice: 66.71 -> 66.17 | Hurt |
| Pretrain on AMOS before fine-tuning | UNet · MSD-Prostate | mDice: 80.64 -> 81.47 | Helped |
| Pretrain on AMOS before fine-tuning | UNet · KiTS | mDice: 88.52 -> 89.93 | Helped |
| Pretrain on AMOS before fine-tuning | UNet · SegTHOR | mDice: 89.66 -> 90.91 | Helped |
| Pretrain on AMOS before fine-tuning | UNet · MSD-Cardiac | mDice: 93.89 -> 93.67 | Hurt |
| Evaluate on unseen scanner domains rather than the ID split | UNet · AMOS-MRI Test | mDice: 86.05 -> 64.70 | Hurt |
| Evaluate on unseen scanner domains rather than the ID split | UNet · AMOS-CT Test | mDice: 88.04 -> 89.67 | No clear effect |
Closest papers by meaning
- Touchstone Benchmark: Are We on the Right Way for Evaluating AI Algorithms for Medical Segmentation? (NeurIPS 2024 · similarity 0.87)
- Versatile Medical Image Segmentation Learned from Multi-Source Datasets via Model Self-Disambiguation (CVPR 2024 · similarity 0.86)
- PanTS: The Pancreatic Tumor Segmentation Dataset (NeurIPS 2025 · similarity 0.85)
- CAT: Coordinating Anatomical-Textual Prompts for Multi-Organ and Tumor Segmentation (NeurIPS 2024 · similarity 0.85)
- Interactive Medical Image Segmentation: A Benchmark Dataset and Baseline (CVPR 2025 · similarity 0.85)
- Pancakes: Consistent Multi-Protocol Image Segmentation Across Biomedical Domains (NeurIPS 2025 · similarity 0.85)
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