Resource-Efficient Medical Image Analysis

Resource-Efficient Medical Image Analysis

First MICCAI Workshop, REMIA 2022, Singapore, September 22, 2022, Proceedings

Cheng, Li; Xu, Xinxing; Petitjean, Caroline; Li, Xiaomeng; Fu, Huazhu; Mahapatra, Dwarikanath

Springer International Publishing AG

09/2022

137

Mole

Inglês

9783031168758

15 a 20 dias

238

Descrição não disponível.
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.
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artificial intelligence;bioinformatics;classification methods;computer networks;computer systems;computer vision;deep learning;image analysis;image processing;image quality;image reconstruction;image segmentation;imaging systems;machine learning;neural networks;pattern recognition