Li / Lv / Huo Multiscale Multimodal Medical Imaging

Third International Workshop, MMMI 2022, Held in Conjunction with MICCAI 2022, Singapore, September 22, 2022, Proceedings
1. Auflage 2022
ISBN: 978-3-031-18814-5
Verlag: Springer International Publishing
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, Band 13594, 131 Seiten, eBook

Reihe: Lecture Notes in Computer Science

This book constitutes the refereed proceedings of the Third International Workshop on Multiscale Multimodal Medical Imaging, MMMI 2022, held in conjunction with MICCAI 2022 in singapore, in September 2022. The 12 papers presented were carefully reviewed and selected from 18 submissions. The MMMI workshop aims to advance the state of the art in multi-scale multi-modal medical imaging, including algorithm development, implementation of methodology, and experimental studies. The papers focus on medical image analysis and machine learning, especially on machine learning methods for data fusion and multi-score learning.

Zielgruppe

Research

Autoren/Hrsg.

Li, Xiang

Lv, Jinglei

Huo, Yuankai

Dong, Bin

Leahy, Richard M.

Li, Quanzheng

Inhaltsverzeichnis

M^2F: Multi-modal and Multi-task Fusion Network for Glioma Diagnosis and Prognosis.- Visual Modalities based Multimodal Fusion for Surgical Phase Recognition.- Cross-scale Attention Guided Multi-instance Learning for Crohn's Disease Diagnosis with Pathological Images.- Vessel Segmentation via Link Prediction of Graph Neural Networks.- A Bagging Strategy-Based Multi-Scale Texture GLCM-CNN Model for Differentiating Malignant from Benign Lesions Using Small Pathologically Proven Dataset.- Liver Segmentation Quality Control in Multi-Sequence MR Studies.- Pattern Analysis of Substantia Nigra in Parkinson Disease by Fifth-Order Tensor Decomposition and Multi-sequence MRI.- Gabor Filter-Embedded U-Net with Transformer-based Encoding for Biomedical Image Segmentation.- Learning-based Detection of MYCN Amplification in Clinical Neuroblastoma Patients: A Pilot Study.- Coordinate Translator for Learning Deformable Medical Image Registration.- Towards Optimal Patch Size in Vision Transformers forTumor Segmentation.- Improve Multi-modal Patch Based Lymphoma Segmentation with Negative Sample Augmentation and Label Guidance on PET/CT scans