CXR-LT 2024 provides a new large chest X-ray benchmark with 45 labels and three tasks, and reports that top models achieve mAP of 0.28 to 0.53 on long-tailed tasks but only 0.11 to 0.13 on zero-shot unseen diseases.
Informatics in radiology: radiology gamuts ontology: differential diagnosis for the semantic web.Radiographics, 34(1):254–264, 2014
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CXR-LT 2024: A MICCAI challenge on long-tailed, multi-label, and zero-shot disease classification from chest X-ray
CXR-LT 2024 provides a new large chest X-ray benchmark with 45 labels and three tasks, and reports that top models achieve mAP of 0.28 to 0.53 on long-tailed tasks but only 0.11 to 0.13 on zero-shot unseen diseases.