Report-supervised (RSuper) learning seeks to alleviate the need for dense tumor voxel labels with constraints derived from radiology reports (e.g., volumes, counts, sizes, locations). In MRI studies of brain tumors, however, we often involve multi-parametric scans and substructures. Here, fine-grained modality/parameter-wise reports are usually provided along with global findings and are correlated with different substructures. Moreover, the reports often describe only the largest lesion and provide qualitative or uncertain cues (``mild,'' ``possible''). Classical RSuper losses (e.g., sum volume consistency) can over-constrain or hallucinate unreported findings under such incompleteness, and are unable to utilize these hierarchical findings or exploit the priors of varied lesion types in a merged dataset. We explicitly parse the global quantitative and modality-wise qualitative findings and introduce a unified, one-sided, uncertainty-aware formulation (MS-RSuper) that: (i) aligns modality-specific qualitative cues (e.g., T1c enhancement, FLAIR edema) with their corresponding substructures using existence and absence losses; (ii) enforces one-sided lower-bounds for partial quantitative cues (e.g., largest lesion size, minimal multiplicity); and (iii) adds extra- vs. intra-axial anatomical priors to respect cohort differences. Certainty tokens scale penalties; missing cues are down-weighted. On 1238 report-labeled BraTS-MET/MEN scans, our MS-RSuper largely outperforms both a sparsely-supervised baseline and a naive RSuper method.

翻译：报告监督学习旨在通过放射学报告（如体积、数量、大小、位置）提供的约束来减轻对密集肿瘤体素标注的需求。然而，在脑肿瘤的磁共振成像研究中，我们通常涉及多参数扫描和子结构。在此类研究中，细粒度的模态/参数特异性报告通常与整体发现一同提供，并与不同的子结构相关联。此外，报告往往仅描述最大病灶并提供定性或不确定的线索（如“轻度”、“可能”）。在这种不完整信息下，经典报告监督损失函数（如总体积一致性）可能过度约束或产生未报告发现的幻觉，且无法利用这些分层发现或在合并数据集中利用不同病灶类型的先验知识。我们明确解析全局定量发现和模态特异性定性发现，并提出一种统一、单边、不确定性感知的建模方法（MS-RSuper），该方法：（i）通过存在性与缺失性损失，将模态特异性定性线索（如T1c增强、FLAIR水肿）与其对应的子结构对齐；（ii）对部分定量线索（如最大病灶尺寸、最小多发性）强制执行单边下界约束；（iii）引入轴外与轴内解剖先验以体现队列差异。确定性标记调节惩罚权重；缺失线索则被降权处理。在1238份带报告标注的BraTS-MET/MEN扫描数据上，我们的MS-RSuper方法显著优于稀疏监督基线和朴素报告监督方法。