Resource-Efficient Medical Image Analysis (eBook, PDF)

First MICCAI Workshop, REMIA 2022, Singapore, September 22, 2022, Proceedings
Redaktion: Xu, Xinxing; Fu, Huazhu; Petitjean, Caroline; Cheng, Li; Mahapatra, Dwarikanath; Li, Xiaomeng

• Format: PDF

Produktbeschreibung

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.

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Produktdetails

• Verlag: Springer International Publishing
• Seitenzahl: 137
• Erscheinungstermin: 15. September 2022
• Englisch
• ISBN-13: 9783031168765
• Artikelnr.: 65713740

Inhaltsangabe

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.