Problem
Research questions and friction points this paper is trying to address.
Microbial Density Stratification
MRI-based
Tumor Assessment
Non-invasive Inference
Patient-level Representation
CHM-Net: Center Heatmap-driven Macro-Micro Modeling Network for MRI-based Microbial Density Stratification
🤖 AI Summary
This study addresses the clinical need for non-invasive assessment of intratumoral microbial density using multimodal MRI by formulating the task, for the first time, as a patient-level representation learning problem. The authors propose a center heatmap-guided mechanism for localizing responses to small lesions, effectively linking imaging phenotypes with microbial states. By integrating localized heatmap responses, the method constructs joint macro–micro evidence to predict microbial density. Combining deep representation learning with multimodal MRI analysis, the approach achieves a 12.06% improvement in accuracy over the strongest baseline on the GBNPC 2026 dataset and demonstrates robust generalization across two additional 3D medical imaging datasets.
📝 Abstract
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/.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Center Heatmap
Macro-Micro Modeling
Microbial Density Stratification
MRI-based Representation Learning
Small-Lesion Localization
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