Research map / Detection and segmentation / Open-set and few-shot detection
CCQ: Cross-Class Query Network for Partially Labeled Organ Segmentation
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| use CCQ with cross-class query learning | 3D UNet for partially labeled organ and tumor segmentation · MOTS (11 categories) | Average Dice: 75.64 -> 76.41 | Helped |
| use CCQ with cross-class query learning | 3D UNet for partially labeled organ and tumor segmentation · MOTS (11 categories) | Average HD: 19.50 -> 16.80 | Helped |
| add cross-class query generation (ablation) | CCQ · MOTS ablation validation | Average Dice: 68.89 -> 72.38 | Helped |
| add cross-class query generation (ablation) | CCQ · MOTS ablation validation | Average HD: 35.02 -> 27.69 | Helped |
| add single-class focused querying (ablation) | CCQ · MOTS ablation validation | Average Dice: 68.91 -> 72.38 | Helped |
| add single-class focused querying (ablation) | CCQ · MOTS ablation validation | Average HD: 34.79 -> 27.69 | Helped |
| concatenate query vectors with image features (ablation) | CCQ · MOTS ablation validation | Average Dice: 71.39 -> 72.38 | Helped |
| concatenate query vectors with image features (ablation) | CCQ · MOTS ablation validation | Average HD: 29.26 -> 27.69 | Helped |
| add image transformer self-attention (ablation) | DoDNet baseline · MOTS ablation validation | Average Dice: 67.81 -> 68.04 | Helped |
| add image transformer self-attention (ablation) | DoDNet baseline · MOTS ablation validation | Average HD: 40.60 -> 40.38 | Helped |
| add attentive skip connections after image self-attention (ablation) | DoDNet baseline · MOTS ablation validation | Average Dice: 68.04 -> 68.68 | Helped |
| add attentive skip connections after image self-attention (ablation) | DoDNet baseline · MOTS ablation validation | Average HD: 40.38 -> 36.07 | Helped |
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