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Medical Image Computing and Computer Assisted Intervention MICCAI 2024 Workshops: LDTM 2024, MMMI/ML4MHD 2024, ML-CDS 2024, Held in Conjunction with MICCAI 2024, Marrakesh, Morocco, October 610, 2024, Proceedings [Minkštas viršelis]

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  • Formatas: Paperback / softback, 262 pages, aukštis x plotis: 235x155 mm, 95 Illustrations, color; 4 Illustrations, black and white; XIX, 262 p. 99 illus., 95 illus. in color., 1 Paperback / softback
  • Serija: Lecture Notes in Computer Science 15401
  • Išleidimo metai: 13-Apr-2025
  • Leidėjas: Springer International Publishing AG
  • ISBN-10: 3031845242
  • ISBN-13: 9783031845246
Kitos knygos pagal šią temą:
Medical Image Computing and Computer Assisted Intervention MICCAI 2024 Workshops: LDTM 2024, MMMI/ML4MHD 2024, ML-CDS 2024, Held in Conjunction with MICCAI 2024, Marrakesh, Morocco, October 610, 2024, ProceedingsDidesnis paveikslėlis
  • Formatas: Paperback / softback, 262 pages, aukštis x plotis: 235x155 mm, 95 Illustrations, color; 4 Illustrations, black and white; XIX, 262 p. 99 illus., 95 illus. in color., 1 Paperback / softback
  • Serija: Lecture Notes in Computer Science 15401
  • Išleidimo metai: 13-Apr-2025
  • Leidėjas: Springer International Publishing AG
  • ISBN-10: 3031845242
  • ISBN-13: 9783031845246
Kitos knygos pagal šią temą:
Pastovi nuoroda: https://www.kriso.lt/db/9783031845246.html
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
LDTM Workshop.- Disease Progression Modelling and Stratification for
detecting sub-trajectories in the natural history of pathologies: application
toParkinsons Disease trajectory modelling.- Back to the Future: Challenges
of Sparse and Irregular Medical Image Time Series.- Individualized
multi-horizon MRI trajectory prediction for Alzheimers Disease.- Toward, for
the Alzheimers 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.