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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

Korean

컴팩트한 고정 유방 X-ray (CXR) 모델은 발견을 지역화 할 수 있지만 상자 언급은 제한적이며 보고 의식 멀티 모드 모델은 추론에 비용이 많이 들었습니다. 우리는 APED-CXR을 소개합니다. 냉동 된 멀티 모드 교사로부터 보고 정보를받은 공간 증거를 11.1M 매개 변수를 가진 학생으로 파괴하고이 지역 내에서 병과 상자 증거 점수를 예측하고 테스트 시간에만 이미지를 요구합니다. 훈련 기간 동안 교사는 엑스레이, 보고 및 쿼리 클래스를 사용하여 학생의 현재 증거지도에서 제안 된 지역을 평가하고 지원 점수는 학생의 집합 증거 점수를위한 훈련 목표로 역할합니다. PadChest에서 훈련하고 옛 PadChest-GR 부분에 평가,

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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