用MRI预测肿瘤微生物密度，新模型提升准确率12%以上。
CHM-Net: Center Heatmap-driven Macro-Micro Modeling Network for MRI-based Microbial Density Stratification
- 通过中心热图定位小病灶响应，建立影像特征与微生物状态关联
- 在GBNPC 2026数据集上比最强基线高出12.06%准确率
- 适用于三维医学图像分类，对小病灶敏感，适合临床辅助决策
微生物密度在肿瘤评估和治疗决策中具有重要临床意义，近期深度学习进展表明可从多模态MRI非侵入性推断。本文首次将基于MRI的微生物密度分层（MRI-MDS）视为患者级表征学习任务，并提出中心热图驱动的宏-微建模网络（CHM-Net）解决该任务。CHM-Net首先通过中心热图引导的小病灶响应定位，建立影像表型与微生物状态之间的联系；在此基础上，从局部热图响应构建患者级宏观-微观证据用于微生物密度预测。在为MRI-MDS构建的新版GBNPC 2026数据集上的实验表明，CHM-Net性能显著优于代表性基线，在准确率上绝对提升12.06%。此外，对两个3D医学图像数据集的辅助验证进一步证明其在体积分类场景中的鲁棒性。
原文摘要 · Abstract (English)
Microbial density is clinically important for tumor assessment and treatment decision-making, and recent advances in deep learning suggest that it can be non-invasively inferred from multimodal MRI. In this work, MRI-based Microbial Density Stratification (MRI-MDS) is first investigated as a patient-level representation learning task, and Center Heatmap-driven Macro-micro modeling Network (CHM-Net) is introduced for this task. CHM-Net first establishes the link between imaging phenotypes and microbial states through center heatmap-guided small-lesion response localization. Building upon this, it constructs patient-level macro-micro evidence from localized heatmap responses for microbial density prediction. Experiments on the novel GBNPC 2026 dataset constructed for MRI-MDS demonstrate the effectiveness of CHM-Net, achieving superior performance over representative baselines with a 12.06% absolute ACC gain over the strongest competing result. Additionally, auxiliary validation on two 3D medical image datasets further verifies its robustness across volumetric medical image classification scenarios. The project is available at https://anonymous.4open.science/r/CHM-Net-942E/.
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