Clinicians trace cephalometric radiographs following a structured anatomical workflow, yet no prior system encodes this into computation. We present a five-phase anatomy-guided pipeline producing confidence-weighted spatial priors that shape HRNet-W32 training, achieving 1.04 mm mean radial error on 25 landmarks across 1,502 radiographs from 7+ imaging devices. A training x inference prior matrix isolates the mechanism: anatomical priors maintain a 1% validation-to-test gap versus 88% without priors (1.94 mm), despite identical validation convergence. The matrix establishes that all trained models are inference-independent, the expanded architecture alone provides no benefit, random priors yield partial but unstable improvement (1.72 mm), and only image-specific anatomically correct priors produce the 1.04 mm result -- functioning as a training-time regularizer requiring no automated prior generation at deployment. Five-fold cross-validation (p=0.0015), patient-level permutation testing (p<0.0001, n=151), quantified Grad-CAM analysis (88% vs. 74% in-zone activation, p<0.001), and clinical measurement validation (skeletal classification kappa=0.79-0.84, zero Class II<->III reversals, ICC>0.95) provide converging evidence. Cross-domain experiments on echocardiography, cervical spine, and hand radiography support the hypothesis that prior effectiveness scales with the spatial entropy of the landmark distribution.
Karlsruhe Institute of Technology（卡尔斯鲁厄理工学院）；Institute for AI in Medicine, University Hospital Essen（埃森大学医院人工智能医学研究所）；University of Duisburg-Essen（杜伊斯堡-埃森大学）；German Cancer Consortium (DKTK)（德国癌症联盟）
SecondOpinion: Anatomy-Aware Gated Reasoning for Efficient Medical Image Analysis
Center for Computational & Data Sciences, Independent University, Bangladesh（孟加拉国独立大学计算与数据科学中心）；Department of Computer Science and Engineering, Independent University, Bangladesh（孟加拉国独立大学计算机科学与工程系）；Graduate School of Engineering, University of Hyogo（兵库大学工程研究生院）
Interpretable Rheumatoid Arthritis Scoring via Anatomy-aware Multiple Instance Learning
Big Data Institute, Nuffield Department of Population Health, University of Oxford, Oxford, UK（大数据研究所，人口健康系，牛津大学，牛津，英国）；Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, UK（骨科、风湿病学和肌肉骨骼科学系，牛津大学，牛津，英国）
Automated Interpretable 2D Video Extraction from 3D Echocardiography
University of California, Los Angeles（加州大学洛杉矶分校）；Kaiser Permanente Division of Research（凯撒医疗集团研究部）；Cedars-Sinai Medical Center（西达赛西医学中心）；Stanford University（斯坦福大学）
CADS: A Comprehensive Anatomical Dataset and Segmentation for Whole-Body Anatomy in Computed Tomography
Department of Quantitative Biomedicine, University of Zurich（定量生物医学系，苏黎世大学）；ETH AI Center, ETH Zurich（ETH人工智能中心，苏黎世联邦理工学院）；Department of Radiation Oncology, University Hospital and University of Zurich（放射肿瘤科，苏黎世大学医院和苏黎世大学）；Faculty of Medicine, University of Zurich（医学系，苏黎世大学）；Department of Neurology and Clinical Neuroscience Center, University Hospital Zurich and University of Zurich（神经科和临床神经科学中心，苏黎世大学医院和苏黎世大学）；International School of Medicine, Istanbul Medipol University（国际医学院，伊斯坦布尔Medipol大学）；School of Computation, Information and Technology, Technical University of Munich（计算、信息与技术学院，慕尼黑技术大学）；Department of Diagnostic and Interventional Neuroradiology, School of Medicine and Health, Technical University of Munich（诊断与介入神经放射科，医学与健康学院，慕尼黑技术大学）；Department of Radiation Oncology, TUM University Hospital Rechts der Isar, TUM School of Medicine and Health, Technical University of Munich（放射肿瘤科，慕尼黑技术大学医院Rechts der Isar，慕尼黑技术大学医学与健康学院）；Institute of Radiation Medicine (IRM), Helmholtz Zentrum München (HMGU)（放射医学研究所（IRM），亥姆霍兹慕尼黑研究中心（HMGU））；German Consortium for Translational Cancer Research (DKTK), Partner Site Munich, Munich, Germany（德国转化癌症研究联盟（DKTK），慕尼黑分部，德国）；University Hospital Essen, Institute of Interventional and Diagnostic Radiology and Neuroradiology（埃森大学医院，介入与诊断放射学及神经放射学研究所）；University Hospital Essen, Institute for Artificial Intelligence in Medicine（埃森大学医院，医学人工智能研究所）；Institute of Diagnostic and Interventional Radiology, Pediatric Radiology and Neuroradiology, University Medical Center Rostock（诊断与介入放射学、儿童放射学及神经放射学研究所，罗斯托克大学医学中心）；Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna（生物医学成像与图像引导治疗系，维也纳医科大学）；Department of Radiology, University Hospital Basel（放射科，巴塞尔大学医院）；Department of Radiology, Cambridge University Hospitals NHS Foundation Trust（放射科，剑桥大学医院 NHS 基础信托）；Labelata GmbH（Labelata 公司）；Visual and Data-Centric Computing, Zuse Institute Berlin (ZIB)（视觉与数据导向计算，柏林 Zuse 研究所（ZIB））；Brigham and Women’s Hospital（布里格姆和女性医院）
