{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/medconv-convolutions-beat-transformers-on","title":"MedConv: Convolutions Beat Transformers on Long-Tailed Bone Density Prediction","arxiv_id":"2502.00631","date":"2025-02-02","proceeding":null,"authors":["Xuyin Qi","Zeyu Zhang","Huazhan Zheng","Mingxi Chen","Numan Kutaiba","Ruth Lim","Cherie Chiang","Zi En Tham","Xuan Ren","Wenxin Zhang","Lei Zhang","Hao Zhang","Wenbing Lv","Guangzhen Yao","Renda Han","Kangsheng Wang","Mingyuan Li","Hongtao Mao","Yu Li","Zhibin Liao","Yang Zhao","Minh-Son To"],"abstract":"Bone density prediction via CT scans to estimate T-scores is crucial, providing a more precise assessment of bone health compared to traditional methods like X-ray bone density tests, which lack spatial resolution and the ability to detect localized changes. However, CT-based prediction faces two major challenges: the high computational complexity of transformer-based architectures, which limits their deployment in portable and clinical settings, and the imbalanced, long-tailed distribution of real-world hospital data that skews predictions. To address these issues, we introduce MedConv, a convolutional model for bone density prediction that outperforms transformer models with lower computational demands. We also adapt Bal-CE loss and post-hoc logit adjustment to improve class balance. Extensive experiments on our AustinSpine dataset shows that our approach achieves up to 21% improvement in accuracy and 20% in ROC AUC over previous state-of-the-art methods.","url_abs":"https://arxiv.org/abs/2502.00631v1","url_pdf":"https://arxiv.org/pdf/2502.00631v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"medconv-convolutions-beat-transformers-on","repo_url":"https://github.com/Richardqiyi/MedConv","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"prediction","task_name":"Prediction"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=2502.00631","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}