Del 13594 - Lecture Notes in Computer Science

Multiscale Multimodal Medical Imaging

Third International Workshop, MMMI 2022, Held in Conjunction with MICCAI 2022, Singapore, September 22, 2022, Proceedings

Häftad, Engelska, 2022

AvXiang Li,Jinglei Lv,Yuankai Huo,Bin Dong,Richard M. Leahy,Quanzheng Li,Jinglei LV

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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.

Produktinformation

Utgivningsdatum2022-10-14
Mått155 x 235 x 8 mm
Vikt224 g
FormatHäftad
SpråkEngelska
SerieLecture Notes in Computer Science
Antal sidor131
FörlagSpringer International Publishing AG
ISBN9783031188138

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Tillämpad datateknik inom Data och IT
Informationsteknik: allmänt inom Data och IT
Systemvetenskap och AI inom Data och IT
Artificiell intelligens inom Data och IT

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

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