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This book constitutes the proceedings from the workshops LDTM 2024, MMMI/ML4MHD 2024, and ML-CDS 2024 which were held in conjunction with the 27th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2024, in Marrakesh, Morocco, in October 2024.
The papers included in this book stem from the following workshops:
- LDTM 2024, Workshop on Longitudinal Disease Tracking and Modeling with Medical Images and Data, which accepted 13 papers from 15 submissions.
- MMMI/ML4MHD 2024, the 5th International Workshop on Multiscale Multimodal Medical Imaging, MMMI 2024, and the First Workshop on Machine Learning for Multimodal/-sensor Healthcare Data, ML4MHD2024, from which 8 papers are included from a total of 14 submissions to the workshop
- ML-CDS 2024, Workshop on Multimodal Learning and Fusion Across Scales for Clinical Decision Support, which accepted 4 papers out of 5 submissions
Table des matières
LDTM Workshop.- Disease Progression Modelling and Stratification for detecting sub-trajectories in the natural history of pathologies: application toParkinson's Disease trajectory modelling.- Back to the Future: Challenges of Sparse and Irregular Medical Image Time Series.- Individualized multi-horizon MRI trajectory prediction for Alzheimer's Disease.- Toward, for the Alzheimer's Disease Neuroimaging Initiative Towards Longitudinal Characterization of Multiple Sclerosis Atrophy Employing SynthSeg Framework and Normative Modeling.- BachCuadraSegHeD: Segmentation of Heterogeneous Data for Multiple SclerosisLesions with Anatomical Constraints.- Longitudinal Segmentation of MS Lesions via Temporal Difference Weighting .- Registration of Longitudinal Liver Examinations for Tumor ProgressAssessment.- Tracking lesion evolution using a Boundary Enhanced Approach for MS change segmentation (BEAMS).- A Radiological-based Coordinate System for the Human Body: A Proof-of-Concept.- MMMI-ML4MHD Workshop.- Language Models Meet Anomaly Detection for Better Interpretabilityand Generalizability.- A Diffusion Model Embedded WCSAU-Net for 3D MRI Brain Tumor Segmentation.- Predicting Human Brain States with Transformer .- Modality Image Quality Prediction for Time-Resolved CT fromBreathing Signals.- RATNUS: Rapid, Automatic Thalamic Nuclei Segmentation using Multimodal MRI inputs.- HyperMM : Robust Multimodal Learning with Varying-sized Inputs.- EMIT: H&E to Multiplex-immunohistochemistry Image Translation with Dual-Branch Pix2pix Generator.- Physics-Informed Latent Diffusion for Multimodal Brain MRI Synthesis.- ML-CDS Workshop.- MedPromptX: Grounded Multimodal Prompting for Chest X-rayDiagnosis.- Predicting Stroke through Retinal Graphs and Multimodal Self-supervised Learning.- Multimodality for Diagnosis of Asian Choroidal Vasculopathy: Resultsfrom a Novel Dataset and Deep-learning Experiments.- Multimodality Frequency Feature Customized Learning for PediatricVentricular Septal Defects Identification.
