Computational Mathematics Modeling in Cancer Analysis

This book constitutes the refereed proceedings of Third International Workshop on Computational Mathematics Modeling in Cancer Analysis, CMMCA 2024, held in Marrakesh, Morocco, on October 6, 2024, in conjunction with MICCAI 2024.

The 12 full papers presented in this book were carefully reviewed and selected from 14 submissions. CMMCA serves as a platform for collaboration among professionals in mathematics, engineering, computer science, and medicine, focusing on innovative mathematical methods for analyzing complex cancer data.

Inhalt

Unified Modeling Enhanced Multimodal Learning for Precision Neuro-Oncolo.- A Reference-based Approach for Tumor Size Estimation in Monocular Laparoscopic ** Videos.-Follicular Lymphoma Grading Based on 3D-DDcGAN and Bayesian CNN using PET-CT Images.- Multi-channel Multi-model Fusion Module (MMFM) based Circulating Abnormal Cells (CACs) Detection for Lung Cancer early Diagnosis with Fluorescence in Situ Hybridization (FISH) Images.- Domain Game: Disentangle Anatomical Feature for Single Domain Generalized Segmentation.- Attention-fusion Model for Multi-Omics (AMMO) Data Integration in Lung Adenocarcinoma.-PD-L1 Expression Prediction using Scalable Multi Instance Transformer.- Improving Single-Source Domain Generalization via Anatomy-Guided Texture Augmentation for Cervical Tumor Segmentation.-PANDA: Pneumonitis ANomaly Detection using Attention U-Net.- Estimating The Average Treatment Effect using Weighting Methods in Lung Cancer Immunotherapy.- Beyond Conventional Parametric Modeling: Data-Driven Framework for Estimation and Prediction of Time Activity Curves in Dynamic PET Imaging.- Assessment of Radiomics Feature Repeatability and Reproducibility and Their Generalizability Across Image Modalities by Perturbation in Nasopharyngeal Carcinoma Patients.**