学习多少，而非仅学习是什么：用于CT视觉-语言预训练的跨患者负担顺序
Learning How Much, Not Just What: Cross-Patient Burden Order for CT Vision-Language Pretraining
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中文总结 AI 辅助
本研究提出Spectrum框架，利用跨患者弱负担顺序补充解剖感知对应关系，无需纵向数据即可学习负担感知CT表示，在CT-RATE和RAD-ChestCT数据集上取得良好性能。
中文摘要 AI 辅助
体积CT视觉-语言预训练可从扫描-报告对中学习3D表示，但全局和解剖感知目标仅监督对应关系：它们确定存在什么，而未约束存在多少。沿一致方向，无法区分同一发现的轻度病例与广泛病例，因此报告中的分级负担语言会坍缩为存在/不存在信号。纵向监督可提供此顺序，但大规模患者匹配CT对稀缺；横断面队列已在不同患者间编码弱负担线索。我们引入Spectrum，一种解剖条件框架，在全研究和器官范围表示每个研究。对于每个器官映射的病理，基于规则的评分器挖掘经置信度过滤的不同患者间从低到高的对，负担方向对齐（BDA）将病理条件图像增量与各范围的报告增量对齐，区分该方向与其反向。因端点为不同个体，目标条件对齐器先使它们可比，故增量反映负担而非患者间差异。BDA进一步区分所选方向与其反向，将其锚定到观察到的高负担端点，并在有序三元组间强制一致性。因每对取自单一病理，BDA旨在约束仅图像-文本对比从未触及的类内结构。Spectrum在CT-RATE上达到85.6的零样本AUROC，在外部RAD-ChestCT上达到72.7，在线性探测和检索中也取得一致增益。因此，弱跨患者顺序是解剖感知对应的可扩展补充，无需纵向数据即可产生负担感知的CT表示。
英文摘要
Volumetric CT vision-language pretraining learns 3D representations from scan-report pairs, but global and anatomy-aware objectives supervise only correspondence: they establish what is present and leave how much unconstrained. Nothing separates a mild from an extensive case of the same finding along a consistent direction, so the graded burden language in reports collapses into a present/absent signal. Longitudinal supervision would supply this order, but patient-matched CT pairs are scarce at scale; cross-sectional cohorts already encode weak burden cues across different patients. We introduce Spectrum, an anatomy-conditioned framework that represents each study at whole-study and organ scopes. For each organ-mapped pathology, a rule-based scorer mines confidence-filtered lower-to-higher pairs of different patients, and Burden-Direction Alignment (BDA) aligns the pathology-conditioned image delta with the report delta at each scope, separating that direction from its reverse. Because the endpoints are different people, a target-conditioned aligner first makes them comparable, so the delta reflects burden rather than between-patient variation. BDA further separates the selected direction from its reverse, anchors it to the observed higher-burden endpoint, and enforces consistency across ordered triplets. Since every pair is drawn within a single pathology, BDA is designed to constrain intra-class structure that image-report contrast alone never touches. Spectrum attains 85.6 zero-shot AUROC on CT-RATE and 72.7 on external RAD-ChestCT, with consistent gains in linear probing and retrieval. Weak cross-patient order is thus a scalable complement to anatomy-aware correspondence, yielding burden-aware CT representations without longitudinal data.
