![]() The lack of large-scale spine X-ray datasets with high-quality images and human expert annotations is the key obstacle. ![]() To the best of our knowledge, no existing studies are devoted to developing and evaluating a comprehensive system for classifying and localizing multiple spine lesions from X-ray scans. The evaluation of spinal bone lesions, however, is a challenging task for radiologists. Radiographs are used as the most critical imaging tool for identifying spine anomalies in clinical practice. PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiologic signals. Goldberger A, Amaral L, Glass L, Hausdorff J, Ivanov PC, Mark R, Mietus JE, Moody GB, Peng CK, Stanley HE. Goldberger, A., Amaral, L., Glass, L., Hausdorff, J., Ivanov, P.C., Mark, R., Mietus, J.E., Moody, G.B., Peng, C.K. "PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiologic signals. Goldberger, A., Amaral, L., Glass, L., Hausdorff, J., Ivanov, P. VinDr-SpineXR: A large annotated medical image dataset for spinal lesions detection and classification from radiographs (version 1.0.0). (2021) 'VinDr-SpineXR: A large annotated medical image dataset for spinal lesions detection and classification from radiographs' (version 1.0.0), PhysioNet. "VinDr-SpineXR: A large annotated medical image dataset for spinal lesions detection and classification from radiographs" (version 1.0.0). Pham, Hieu Huy, Nguyen Trung, Hieu, and Ha Quy Nguyen.
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