Resource-Efficient Medical Image Analysis  First MICCAI Workshop, REMIA 2022, Singapore, September 22, 2022, Proceedings /  [electronic resource] : 
edited by Xinxing Xu, Xiaomeng Li, Dwarikanath Mahapatra, Li Cheng, Caroline Petitjean, Huazhu Fu. 
 - 1st ed. 2022. 
 - X, 137 p. 42 illus., 39 illus. in color.  online resource. 
 - Lecture Notes in Computer Science,  13543  1611-3349 ; .
 - Lecture Notes in Computer Science,  13543 .
<br/><br/>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. 
<br/><br/> 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. 
<br/><br/><label>ISBN: </label>9783031168765 
<br/><br/><label>Standard No.: </label>10.1007/978-3-031-16876-5  doi 
<br/><br/><label>Subjects--Topical Terms: </label><br/>Image processing--Digital techniques.<br/>Computer vision.<br/>Artificial intelligence.<br/>Education--Data processing.<br/>Social sciences--Data processing.<br/>Computer Imaging, Vision, Pattern Recognition and Graphics.<br/>Artificial Intelligence.<br/>Computers and Education.<br/>Computer Application in Social and Behavioral Sciences.
<br/><br/><label>LC Class. No.: </label>TA1501-1820 TA1634 
<br/><br/><label>Dewey Class. No.: </label>006