Imaging Systems for GI Endoscopy, and Graphs in Biomedical Image Analysis: First MICCAI Workshop, ISGIE 2022, and Fourth MICCAI Workshop, GRAIL 2022, Held in Conjunction with MICCAI 2022, Singapore, S

ISBN-13: 9783031210822 / Angielski / Miękka / 2022 / 129 str.

Luigi Manfredi; Seyed-Ahmad Ahmadi; Michael Bronstein

Opis

This book constitutes the refereed proceedings of the first MICCAI Workshop, ISGIE 2022, Imaging Systems for GI Endoscopy, and the Fourth MICCAI Workshop, GRAIL 2022, GRaphs in biomedicAL Image and analysis, held in conjunction with MICCAI 2022, Singapore, September 18, 2022.

ISGIE 2022 accepted 6 papers from the 8 submissions received.This workshop focuses on novel scientific contributions to vision systems, imaging algorithms as well as the autonomous system for endorobot for GI endoscopy. This includes lesion and lumen detection, as well as 3D reconstruction of the GI tract and hand-eye coordination.

 GRAIL 2022 accepted 6 papers from the 10 submissions received. The workshop aims to bring together scientists that use and develop graph-based models for the analysis of biomedical images and to encourage the exploration of graph-based models for difficult clinical problems within a variety of biomedical imaging contexts.

Spis treści

Imaging Systems for GI Endoscopy.- Light Adaptation for Classification of the Upper Gastrointestinal Sites.- Criss-Cross Attention based Multi-Level Fusion Network for Gastric Intestinal Metaplasia Segmentation.- Colonoscopy Landmark Detection using Vision Transformers.- Real-Time Lumen Detection for Autonomous Colonoscopy.- SuperPoint Features in Endoscopy.- Estimating the Coverage in 3D Reconstructions of the Colon from Colonoscopy Videos.- Graphs in Biomedical Image Analysis.- Modular Graph Encoding and Hierarchical Readout for Functional Brain Network based eMCI Diagnosis.- Bayesian Filtered Generation of Post-surgical Brain Connectomes on Tumor Patients.- Deep Cross-Modality and Resolution Graph Integration for Universal Brain Connectivity Mapping and Augmentation.- Using Hierarchically Connected Nodes and Multiple GNN Message Passing Steps to Increase the Contextual Information in Cell-Graph Classification.- TaG-Net: Topology-aware Graph Network for Vessel Labeling.- Transforming connectomes to “any” parcellation via graph matching.