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This book constitutes the refereed proceedings of the 4th International Workshop on Computational Mathematics Modeling in Cancer Analysis, CMMCA 2025, held in Daejeon, South Korea, during September 27, 2025, in conjunction with MICCAI 2025.
The 17 full papers presented in this book were carefully reviewed and selected from 24 submissions. These papers focus on algorithmic and mathematical innovations that advance cancer imaging and analysis across spatial, temporal, and biological scales.
Inhaltsverzeichnis
.- A Lightweight Optimization Framework for Estimating 3D Brain Tumor Infiltration.
.- A Data-Driven Approach to Optimise Parameters of a Computational Digital Twin Model in Response to SBRT on MR-Linac.
.- FMIC-AI: Annotation-Free Tumor Cell Detection in Fluorescence Microscopy via Self-Supervised Anomaly Detection.
.- Score-based Diffusion Model for Unpaired Virtual Histology Staining.
.- Redefining Spectral Unmixing for In-Vivo BrainTissue Analysis from Hyperspectral Imaging.
.- CT Image Segmentation Using Frequency Domain Feature-Assisted Selective Long Memory State Space Model.
.- Towards Robust Skin Lesion Classification: Lesion Segmentation, Mole Collision Simulation and Hierarchical learning.
.- Key Clinical Parameters Detection and Ovarian Tumor Benign/Malignant Classification in Multi-Modal Ultrasound Images via a Multi-Task Model.
.- OG-SAM: Enhancing Multi-Organ Segmentation with Organogenesis-Based Adaptive Modeling.
.- CoMoSeg: Anatomical Consistency and Cross Modality Guidance for Robust Brain Tumor Segmentation Using Partially Labeled MR Sequences.
.- Region-aware Diagnosis of Clinically Significant Prostate Cancer via Semi-supervised Learning Segmentation.
.- GraphMMP: A Graph Neural Network Model with Mutual Information and Global Fusion for Multimodal Medical Prognosis.
.- Dual-Guided 3D Liver CT Image Generation for Medical Analysis.
.- HaDM-ST: Histology-Assisted Differential Modeling for Spatial Transcriptomics Generation.
.- Projection-Driven Robust Motion Compensation for CBCT Using a Patient-Specific Model Learned from Prior Scans.
.- Revealing New Possibilities for Breast MRI Enhancement: Mamba-Driven Cross-Attention GAN with VMKANet.
.- Hierarchical Brain Structure Modeling for Predicting Genotype of Glioma.
Zusammenfassung
This book constitutes the refereed proceedings of the 4th International Workshop on Computational Mathematics Modeling in Cancer Analysis, CMMCA 2025, held in Daejeon, South Korea, during September 27, 2025, in conjunction with MICCAI 2025.
The 17 full papers presented in this book were carefully reviewed and selected from 24 submissions. These papers focus on algorithmic and mathematical innovations that advance cancer imaging and analysis across spatial, temporal, and biological scales.
