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xu xinxing (curatore); li xiaomeng (curatore); mahapatra dwarikanath (curatore); cheng li (curatore); petitjean caroline (curatore); fu huazhu (curatore) - resource-efficient medical image analysis

Resource-Efficient Medical Image Analysis First MICCAI Workshop, REMIA 2022, Singapore, September 22, 2022, Proceedings

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Dettagli

Genere:Libro
Lingua: Inglese
Editore:

Springer

Pubblicazione: 09/2022
Edizione: 1st ed. 2022





Trama

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.





Sommario

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.











Altre Informazioni

ISBN:

9783031168758

Condizione: Nuovo
Collana: Lecture Notes in Computer Science
Dimensioni: 235 x 155 mm
Formato: Brossura
Illustration Notes:X, 137 p. 42 illus., 39 illus. in color.
Pagine Arabe: 137
Pagine Romane: x


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