Xu / Li / Mahapatra Resource-Efficient Medical Image Analysis

First MICCAI Workshop, REMIA 2022, Singapore, September 22, 2022, Proceedings
1. Auflage 2022
ISBN: 978-3-031-16876-5
Verlag: Springer International Publishing
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, Band 13543, 137 Seiten, eBook

Reihe: Lecture Notes in Computer Science

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.

Zielgruppe

Research

Autoren/Hrsg.

Xu, Xinxing

Li, Xiaomeng

Mahapatra, Dwarikanath

Cheng, Li

Petitjean, Caroline

Fu, Huazhu

Inhaltsverzeichnis

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.