Cantitate/Preț
Produs
Total produse
Vezi coșul
Resource-Efficient Medical Image Analysis: First MICCAI Workshop, REMIA 2022, Singapore, September 22, 2022, Proceedings de Xinxing Xu

Resource-Efficient Medical Image Analysis: First MICCAI Workshop, REMIA 2022, Singapore, September 22, 2022, Proceedings: Lecture Notes in Computer Science, cartea 13543

Editat de Xinxing Xu, Xiaomeng Li, Dwarikanath Mahapatra, Li Cheng, Caroline Petitjean, Huazhu Fu
en Limba Engleză Paperback – 11 sep 2022
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.
Citește tot Restrânge

Din seria Lecture Notes in Computer Science

Preț: 34013 lei

Preț vechi: 42516 lei
-20% Nou

Puncte Express: 510
Preț estimativ în valută:
6019€ • 7058$ • 5286£

Carte tipărită la comandă

Livrare economică 04-18 februarie 26

 Adaugă în coș

Wish list
Gift list
Am citit!
Adaugă în listă
Preluare comenzi: 021 569.72.76

Specificații

ISBN-13: 9783031168758
ISBN-10: 3031168755
Pagini: 137
Ilustrații: X, 137 p. 42 illus., 39 illus. in color.
Dimensiuni: 155 x 235 mm
Greutate: 0.22 kg
Ediția:1st ed. 2022
Editura: Springer Nature Switzerland
Colecția Springer
Seria Lecture Notes in Computer Science

Locul publicării:Cham, Switzerland

Cuprins

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.