Resource-Efficient Medical Image Analysis : First MICCAI Workshop, REMIA 2022, Singapore, September 22, 2022, Proceedings / edited by Xinxing Xu, Xiaomeng Li, Dwarikanath Mahapatra, Li Cheng, Caroline Petitjean, Huazhu Fu.

Format:

Book

Contributor:

Xu, Xinxing, editor.

Series:

Lecture Notes in Computer Science, 1611-3349 ; 13543

Language:

English

Subjects (All):

Image processing--Digital techniques.

Image processing.

Computer vision.

Artificial intelligence.

Education--Data processing.

Education.

Social sciences--Data processing.

Social sciences.

Computer Imaging, Vision, Pattern Recognition and Graphics.

Artificial Intelligence.

Computers and Education.

Computer Application in Social and Behavioral Sciences.

Local Subjects:

Computer Imaging, Vision, Pattern Recognition and Graphics.

Artificial Intelligence.

Computers and Education.

Computer Application in Social and Behavioral Sciences.

Physical Description:

1 online resource (148 pages)

Edition:

1st ed. 2022.

Place of Publication:

Cham : Springer Nature Switzerland : Imprint: Springer, 2022.

Summary:

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.

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.

Notes:

Includes bibliographical references and index.

Other Format:

Print version: Xu, Xinxing Resource-Efficient Medical Image Analysis

ISBN:

9783031168765

3031168763