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APED-CXR: Adaptive Privileged Evidence Distillation for Chest X-Ray Grounding with Image-Only Inference

Md Shihabul Islam Shovo, Nishi Kanta Paul, Jamil Fayyad · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.29740 · cited by 0 works (OpenAlex) · open-access PDF

Chinese

紧凑的固定胸部X射线(CXR)模型将发现局部化,但盒子注释是有限的,报告意识的多式模型是昂贵的推断。我们引入了APED-CXR,该模型将报告信息的空间证据从冻结的多式教师分配到具有11.1M参数的学生中,该模型预测来自共享证据地图的疾病和盒子,并需要在测试时间内单独的图像。在培训过程中,教师使用XR、报告和询问类来评估学生目前的证据地图所提出的区域,其支持分数作为学生在这些区域内的综合证据分数的培训目标。在PadChest上训练,并评估旧的PadChest-GR部分,更新学生对教师评估的建议,从0.302

Machine translation (Meta M2M100); the English original follows.

Abstract

Compact chest X-ray (CXR) models that localize findings suit deployment, but box annotations are limited and report-aware multimodal models are expensive at inference. We introduce APED-CXR, which distills report-informed spatial evidence from a frozen multimodal teacher into a student with 11.1M parameters that predicts diseases and boxes from shared evidence maps and requires the image alone at test time. During training, the teacher uses the radiograph, report, and queried class to evaluate regions proposed from the student's current evidence maps, and its support scores serve as training targets for the student's pooled evidence scores within these regions. Trained on PadChest and evaluated on held-out PadChest-GR, refreshing the student's proposals for teacher evaluation improves box-level localization F1 from 0.302 with fixed warm-up proposals to 0.338 (IoU >= 0.5; gain 0.036, 95% CI [0.022, 0.049]), while maintaining diagnostic AUC. This gain depends on the teacher checking current proposals: with equally many random boxes, a fixed dense box set, teacher-generated boxes, or report rules instead of the teacher, F1 stays near the fixed-proposal level, and proposals two refreshes old recover only part of the gain. On NIH ChestX-ray14, a secondary test set, box recall also rises from 0.423 to 0.447.

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Topics: Inference, Computer science, Class (philosophy), Test (biology), Artificial intelligence, Machine learning

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