Resource-Efficient Medical Image Analysis - First MICCAI Workshop, REMIA 2022, Singapore, September 22, 2022, Proceedings

This book constitutes the refereed proceedings of the first MICCAI Workshop on Resource-Efficient Medical Image Analysis, REMIA 2022, held in conjunction with MICCAI 2022, in September 2022 as a hybrid event.
REMIA 2022 accepted 13 papers from the 19 submissions received. The workshop aims at creating a discussion on the issues for practical applications of medical imaging systems with data, label and hardware limitations.

List of contents

Multi-Task Semi-Supervised Learning for Vascular Network.- Segmentation and Renal Cell Carcinoma Classification.- Self-supervised Antigen Detection Artificial Intelligence (SANDI).- RadTex: Learning Effcient Radiograph Representations from Text Reports.- Single Domain Generalization via Spontaneous Amplitude Spectrum Diversification.- Triple-View Feature Learning for Medical Image Segmentation.- Classification of 4D fMRI Images Using ML, Focusing on Computational and Memory Utilization Effciency.- An Effcient Defending Mechanism Against Image Attacking On Medical Image Segmentation Models.- Leverage Supervised and Self-supervised Pretrain Models for Pathological Survival Analysis via a Simple and Low-cost Joint Representation Tuning.- Pathological Image Contrastive Self-Supervised Learning.- Investigation of Training Multiple Instance Learning Networks with Instance Sampling.- Masked Video Modeling with Correlation-aware Contrastive Learning for Breast Cancer Diagnosis in Ultrasound.- A self-attentive meta-learning approach for image-based few-shot disease detection.- Facing Annotation Redundancy: OCT Layer Segmentation with Only 10 Annotated Pixels Per Layer.

Product details

• Assisted by: Li Cheng (Editor), Huazhu Fu (Editor), Xiaomeng Li (Editor), Dwarikanath Mahapatra (Editor), Dwarikanath Mahapatra et al (Editor), Caroline Petitjean (Editor), Xinxing Xu (Editor)
• Publisher: Springer, Berlin
• Languages: English
• Product format: Paperback / Softback
• Released: 11.09.2022
• EAN: 9783031168758
• ISBN: 978-3-0-3116875-8
• No. of pages: 137
• Dimensions: 155 mm x 8 mm x 235 mm
• Illustrations: X, 137 p. 42 illus., 39 illus. in color.
• Series: Lecture Notes in Computer Science
• Subject: Natural sciences, medicine, IT, technology > IT, data processing > Application software