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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

Japanese

コンパクトの固定胸のX線(CXR)モデルは、発見をローカル化し、ボックスの注釈は限られ、レポート認識の多形モデルは推論で高価です。我々は、レポート情報を提供した空間証拠を凍結した多形教師から111Mパラメータを持つ学生に分散するAPED-CXRモデルを導入し、これらの地域内の学生の集計証拠のスコアのためのトレーニングターゲットとして機能します。PadChestで訓練され、テスト時だけに画像を評価する必要があります。トレーニング中に、教師は、レポート、レポート、クエストレートを用いて、学生の現在の証拠マップから提案された地域を評価し、そのサポートスコアは、学生の集計証

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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