Research map / Label-efficient and robust learning / Recognition and long-tail
XProtoNet: Diagnosis in Chest Radiography With Global and Local Explanations
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| use adaptive disease-specific occurrence areas instead of fixed-size prototype patches | XProtoNet with ResNet-50 · NIH Chest X-ray, official test split | mean AUC: 0.792 -> 0.820 | Helped |
| use adaptive disease-specific occurrence areas instead of global average pooling | XProtoNet with ResNet-50 · NIH Chest X-ray, official test split | mean AUC: 0.789 -> 0.820 | Helped |
| add transformation consistency loss L_trans (ablation) | XProtoNet · NIH Chest X-ray, official test split | mean AUC: 0.815 -> 0.820 | Helped |
| constrain prototypes to features inside annotated bounding boxes (ablation) | XProtoNet · NIH Chest X-ray, five-fold cross-validation | mean AUC: 0.849 -> 0.850 | No clear effect |
Closest papers by meaning
- CXReasonBench: A Benchmark for Evaluating Structured Diagnostic Reasoning in Chest X-rays (NeurIPS 2025 · similarity 0.84)
- Bootstrapping Chest CT Image Understanding by Distilling Knowledge from X-ray Expert Models (CVPR 2024 · similarity 0.83)
- Novel Class Discovery in Chest X-rays via Paired Images and Text (AAAI 2024 · similarity 0.83)
- Learning Low-Rank Feature for Thorax Disease Classification (NeurIPS 2024 · similarity 0.83)
- BenchX: A Unified Benchmark Framework for Medical Vision-Language Pretraining on Chest X-Rays (NeurIPS 2024 · similarity 0.82)
- Large-scale and Fine-grained Vision-language Pre-training for Enhanced CT Image Understanding (ICLR 2025 · similarity 0.81)
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