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What is Chen OralXrays-9 Towards Hospital-Scale Panoramic X-Ray Anomaly Detection Via Personalized Multi-Object CVPR 2025 Paper about?

The paper introduces the OralXrays-9 dataset, a comprehensive benchmark for panoramic X-ray images aimed at improving automated oral anomaly detection, containing 12,688 images with 84,113 annotated instances of nine common anomalies. It proposes a novel Multi-Object Query-Aware Mining (MOQAM) paradigm that integrates advanced mechanisms to tackle challenges such as multi-scale variations and class imbalances in the dataset. The study demonstrates the effectiveness of MOQAM in enhancing diagnostic capabilit

Author

narutocaodo

Language

EN