Data Engineering in Medical Imaging〈1st ed. 2023〉 : First MICCAI Workshop, DEMI 2023, Held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, October 8, 2023, Proceedings

著者名：Bhattarai, Binod (EDT)/Ali, Sharib (EDT)/Rau, Anita (EDT)/Nguyen, Anh (EDT)/Namburete, Ana (EDT)/Caramalau, Razvan (EDT)

Springer（2023/09/30発売）

• 言語：ENG
• ISBN：9783031449918
• eISBN：9783031449925

Description

​Volume LNCS 14414 constitutes the refereed proceedings of the 26th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2023, which was held in Vancouver, Canada in October 2023.

The DEMI 2023 proceedings contain 11 high-quality papers of 9 to 15 pages pre-selected through a rigorous peer review process (with an average of three reviews per paper). All submissions were peer-reviewed through a double-blind process by at least three members of the scientific review committee, comprising 16 experts in the field of medical imaging. The accepted manuscripts cover various medical image analysis methods and applications.

Table of Contents

Weakly Supervised Medical Image Segmentation through Dense Combinations of Dense Pseudo-Labels.- Whole Slide Multiple Instance Learning for Predicting Axillary Lymph Node Metastasis.- A Client-server Deep Federated Learning for Cross-domain Surgical Image Segmentation.- Pre-training with simulated ultrasound images for breast mass segmentation and classification.- Efficient Large Scale Medical Image Dataset Preparation for Machine Learning Applications.- A Self-supervised Approach for Detecting the Edges of Haustral Folds in Colonoscopy Video.- Procedurally Generated Colonoscopy and Laparoscopy Data For Improved Model Training Performance.- Improving Medical Image Classification in Noisy Labels Using Only Self-supervised Pretraining.- A Study on Using Transformer Encoding Techniques to Optimize Data-driven Volume-to-Surface Registration for Minimally Invasive Liver Interventions.- Vision Transformer-based Self-Supervised Learning for Ulcerative Colitis Grading in Colonoscopy.- Task-guided Domain Gap Reduction for Monocular Depth Prediction in Endoscopy.