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

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.

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.