发表机构
School of Computer Science and Engineering; School of Future Technology; Department of Medical Imaging; Affiliated Cancer Hospital, Guangzhou Medical University（计算机科学与工程学院; 未来技术学院; 医学影像科; 广州医学院附属癌症医院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究基于MRI的微生物密度分层，提出CHM-Net，通过中心热图引导小病变反应定位，构建宏观-微观证据预测微生物密度，在GBNPC 2026数据集上验证有效性，在两个3D医学图像数据集上证实鲁棒性。
AI 中文摘要
微生物密度对肿瘤评估和治疗决策具有临床重要性，深度学习的进展表明可从多模态MRI无创推断。本文首次将基于MRI的微生物密度分层（MRI-MDS）作为患者级表征学习任务研究，并引入中心热图驱动的宏观-微观建模网络（CHM-Net）。CHM-Net通过中心热图引导的小病变反应定位建立成像表型与微生物状态的联系，基于此构建患者级宏观-微观证据进行微生物密度预测。在为MRI-MDS构建的新型GBNPC 2026数据集上的实验证明了CHM-Net的有效性，在绝对ACC上比最强竞争结果提高了12.06%，在两个3D医学图像数据集上的辅助验证进一步证实了其在体积医学图像分类场景中的鲁棒性。
英文摘要
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/.
