{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T02:02:20Z","timestamp":1740103340276,"version":"3.37.3"},"publisher-location":"New York, NY, USA","reference-count":16,"publisher":"ACM","content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2024,6,21]]},"DOI":"10.1145\/3690407.3690430","type":"proceedings-article","created":{"date-parts":[[2024,10,24]],"date-time":"2024-10-24T18:55:28Z","timestamp":1729796128000},"page":"134-138","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Improved YOLO5 for diagnosis of developmental dysplasia of the hip"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-0754-4748","authenticated-orcid":false,"given":"Kunhao","family":"Chen","sequence":"first","affiliation":[{"name":"Guangdong University of Technology, Guangzhou, Guangdong, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5206-1110","authenticated-orcid":false,"given":"Jing","family":"Chen","sequence":"additional","affiliation":[{"name":"Guangdong University of Technology, Guangzhou, Guangdong, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-1092-8916","authenticated-orcid":false,"given":"Xiaoyou","family":"Fan","sequence":"additional","affiliation":[{"name":"Guangdong University of Technology, Guangzhou, Guangdong, China"}]}],"member":"320","published-online":{"date-parts":[[2024,10,24]]},"reference":[{"key":"e_1_3_3_1_1_2","unstructured":"Tnnis D. [Indications and time planning for operative interventions in hip dysplasia in child and adulthood]. Ztschrift F\u00fcr Orthopdie Und Ihre Grenzgebiete 1985;123:458."},{"key":"e_1_3_3_1_2_2","volume-title":"Learning-based landmark detection in pelvis x-rays with attention mechanism: data from the osteoarthritis initiative","author":"Pei Y","year":"2023","unstructured":"Pei Y, Mu L, Xu C, et al. Learning-based landmark detection in pelvis x-rays with attention mechanism: data from the osteoarthritis initiative. Biomed Phys Eng Express 2023;9."},{"key":"e_1_3_3_1_3_2","first-page":"3663","volume":"202","author":"Chen Z","unstructured":"Chen Z, Yang C, Zhu M, et al. Personalized Retrogress-Resilient Federated Learning Toward Imbalanced Medical Data. IEEE Trans Med Imaging 2022;41:3663-74.","journal-title":"Personalized Retrogress-Resilient Federated Learning Toward Imbalanced Medical Data. IEEE Trans Med Imaging"},{"key":"e_1_3_3_1_4_2","first-page":"155","volume":"201","author":"Sahin S","unstructured":"Sahin S, Akata E, Sahin O, et al. A novel computer-based method for measuring the acetabular angle on hip radiographs. Acta Orthop Traumatol Turc 2017;51:155-9.","journal-title":"Acta Orthop Traumatol Turc"},{"key":"e_1_3_3_1_5_2","volume-title":"editors","author":"Agomma RO","year":"2018","unstructured":"Agomma RO, Guise JD, V\u00e1zquez C, et al., editors. Automatic detection of anatomical regions in frontal x-ray images: comparing convolutional neural networks to random forest. Computer-Aided Diagnosis; 2018."},{"key":"e_1_3_3_1_6_2","doi-asserted-by":"crossref","unstructured":"Xu W Shu L Gong P et al. A Deep-Learning Aided Diagnostic System in Assessing Developmental Dysplasia of the Hip on Pediatric Pelvic Radiographs. Frontiers in pediatrics 2021;9:785480.","DOI":"10.3389\/fped.2021.785480"},{"key":"e_1_3_3_1_7_2","first-page":"744","volume":"202","author":"Chen J","unstructured":"Chen J, Fan X, Chen Z, et al. Enhancing YOLO5 for the Assessment of Irregular Pelvic Radiographs with Multimodal Information. J Imaging Inform Med 2024;37:744-55.","journal-title":"J Imaging Inform Med"},{"key":"e_1_3_3_1_8_2","first-page":"2999","volume":"201","author":"Lin TY","unstructured":"Lin TY, Goyal P, Girshick R, et al. Focal Loss for Dense Object Detection. IEEE Transactions on Pattern Analysis & Machine Intelligence 2017;PP:2999-3007.","journal-title":"Focal Loss for Dense Object Detection. IEEE Transactions on Pattern Analysis & Machine Intelligence"},{"key":"e_1_3_3_1_9_2","volume":"202","author":"Chen K","unstructured":"Chen K, Lei W, Hu P, et al. PCCT: Progressive Class-Center Triplet Loss for Imbalanced Medical Image Classification. IEEE J Biomed Health Inform 2023;PP.","journal-title":"IEEE J Biomed Health Inform"},{"key":"e_1_3_3_1_10_2","first-page":"146","volume":"202","author":"Zhang Y-F","unstructured":"Zhang Y-F, Ren W, Zhang Z, et al. Focal and efficient IOU loss for accurate bounding box regression. Neurocomputing 2022;506:146-57.","journal-title":"Neurocomputing"},{"key":"e_1_3_3_1_11_2","volume-title":"Generalized Intersection over Union: A Metric and A Loss for Bounding Box Regression","author":"Rezatofighi H","year":"2019","unstructured":"Rezatofighi H, Tsoi N, Gwak JY, et al. Generalized Intersection over Union: A Metric and A Loss for Bounding Box Regression. IEEE 2019."},{"key":"e_1_3_3_1_12_2","volume-title":"Distance-IoU Loss: Faster and Better Learning for Bounding Box Regression. arXiv","author":"Zheng Z","year":"2019","unstructured":"Zheng Z, Wang P, Liu W, et al. Distance-IoU Loss: Faster and Better Learning for Bounding Box Regression. arXiv 2019."},{"key":"e_1_3_3_1_13_2","volume-title":"editors","author":"Redmon J","year":"2016","unstructured":"Redmon J, Divvala S, Girshick R, et al., editors. You Only Look Once: Unified, Real-Time Object Detection. Computer Vision & Pattern Recognition; 2016."},{"key":"e_1_3_3_1_14_2","volume-title":"Yolov4 release","author":"Bochkovskiy A.","year":"2020","unstructured":"Bochkovskiy. A. Yolov4 release. 2020."},{"key":"e_1_3_3_1_15_2","volume-title":"Yolov5 release v7.0","author":"Glenn J.","year":"2022","unstructured":"Glenn J. Yolov5 release v7.0. 2022."},{"key":"e_1_3_3_1_16_2","volume":"201","author":"Hu J","unstructured":"Hu J, Shen L, Sun G, et al. Squeeze-and-Excitation Networks. IEEE Transactions on Pattern Analysis and Machine Intelligence 2017;PP.","journal-title":"Squeeze-and-Excitation Networks. IEEE Transactions on Pattern Analysis and Machine Intelligence"}],"event":{"name":"CAIBDA 2024: 2024 4th International Conference on Artificial Intelligence, Big Data and Algorithms","acronym":"CAIBDA 2024","location":"Zhengzhou China"},"container-title":["Proceedings of the 2024 4th International Conference on Artificial Intelligence, Big Data and Algorithms"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3690407.3690430","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,1,14]],"date-time":"2025-01-14T22:33:46Z","timestamp":1736894026000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3690407.3690430"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,6,21]]},"references-count":16,"alternative-id":["10.1145\/3690407.3690430","10.1145\/3690407"],"URL":"https:\/\/doi.org\/10.1145\/3690407.3690430","relation":{},"subject":[],"published":{"date-parts":[[2024,6,21]]},"assertion":[{"value":"2024-10-24","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}