Cantitate/Preț
Produs
Total produse
Vezi coșul
Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries: 7th International Workshop, BrainLes 2021, Held in Conjunction with MICCAI 2021, Virtual Event, September 27, 2021, Revised Selected Papers, Part II de Alessandro Crimi

Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries: 7th International Workshop, BrainLes 2021, Held in Conjunction with MICCAI 2021, Virtual Event, September 27, 2021, Revised Selected Papers, Part II: Lecture Notes in Computer Science, cartea 12963

Editat de Alessandro Crimi, Spyridon Bakas
en Limba Engleză Paperback – 15 iul 2022
This two-volume set LNCS 12962 and 12963 constitutes the thoroughly refereed proceedings of the 7th International MICCAI Brainlesion Workshop, BrainLes 2021, as well as the RSNA-ASNR-MICCAI Brain Tumor Segmentation (BraTS) Challenge, the Federated Tumor Segmentation (FeTS) Challenge, the Cross-Modality Domain Adaptation (CrossMoDA) Challenge, and the challenge on Quantification of Uncertainties in Biomedical Image Quantification (QUBIQ). These were held jointly at the 23rd Medical Image Computing for Computer Assisted Intervention Conference, MICCAI 2020, in September 2021. The 91 revised papers presented in these volumes were selected form 151 submissions. Due to COVID-19 pandemic the conference was held virtually.
Citește tot Restrânge

Toate formatele și edițiile

Toate formatele și edițiile Preț Express
Paperback (6) 33023 lei  3-5 săpt.
  Springer International Publishing – 17 mai 2020 33023 lei  3-5 săpt.
  Springer International Publishing – 17 mai 2020 33023 lei  3-5 săpt.
  Springer International Publishing – 26 mar 2021 70013 lei  6-8 săpt.
  Springer International Publishing – 27 mar 2021 70046 lei  6-8 săpt.
  Springer International Publishing – 22 iul 2022 81037 lei  6-8 săpt.
  Springer International Publishing – 15 iul 2022 98298 lei  6-8 săpt.

Din seria Lecture Notes in Computer Science

Preț: 98298 lei

Preț vechi: 122872 lei
-20% Nou

Puncte Express: 1474
Preț estimativ în valută:
18810€ • 19864$ • 15653£

Carte tipărită la comandă

Livrare economică 11-25 ianuarie 25

 Adaugă în coș

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

Specificații

ISBN-13: 9783031090011
ISBN-10: 3031090012
Pagini: 601
Ilustrații: XXIII, 601 p. 225 illus., 195 illus. in color.
Dimensiuni: 155 x 235 mm
Greutate: 0.87 kg
Ediția:1st ed. 2022
Editura: Springer International Publishing
Colecția Springer
Seria Lecture Notes in Computer Science

Locul publicării:Cham, Switzerland

Cuprins

BiTr-Unet: a CNN-Transformer Combined Network for MRI Brain Tumor Segmentation.- Optimized U-Net for Brain Tumor Segmentation.- MS UNet: Multi-Scale 3D UNet for Brain Tumor Segmentation.- Evaluating Scale Attention Network for Automatic Brain Tumor Segmentation with Large Multi-parametric MRI Database.- Orthogonal-Nets: A large ensemble of 2D neural networks for 3D Brain Tumor Segmentation.- Feature Learning by Attention and Ensemble with 3D U-Net to Glioma Tumor Segmentation.- MRI Brain Tumor Segmentation Using Deep Encoder-Decoder Convolutional Neural Networks.- Brain Tumor Segmentation with Patch-based 3D Attention UNet from Multi-parametric MRI.- Dice Focal Loss with ResNet-like Encoder-Decoder architecture in 3D Brain Tumor Segmentation.- HNF-Netv2 for Brain Tumor Segmentation using multi-modal MR Imaging.- Disparity Autoencoders for Multi-class Brain Tumor Segmentation.- Disparity Autoencoders for Multi-class Brain Tumor Segmentation.- Disparity Autoencoders for Multi-class BrainTumor Segmentation.- Brain Tumor Segmentation in Multi-parametric Magnetic Resonance Imaging using Model Ensembling and Super-resolution.- Quality-aware Model Ensemble for Brain Tumor Segmentation.- Redundancy Reduction in Semantic Segmentation of 3D Brain Tumor MRIs.- An Ensemble Approach to Automatic Brain Tumor Segmentation.- Extending nn-UNet for brain tumor segmentation.- Generalized Wasserstein Dice Loss, Test-time Augmentation, and Transformers for the BraTS 2021 challenge.- Coupling nnU-Nets with Expert Knowledge for Accurate Brain Tumor Segmentation from MRI.- Deep Learning based Ensemble Approach for 3D MRI Brain Tumor Segmentation.- Prediction of MGMT Methylation Status of Glioblastoma using Radiomics and Latent Space Shape Features.- bining CNNs With Transformer for Multimodal 3D MRI Brain Tumor Segmentation.- Automatic Brain Tumor Segmentation with a Bridge-Unet deeply supervised enhanced with downsampling pooling combination, Atrous Spatial Pyramid Pooling, Squeeze-and-Excitation and EvoNorm.

Caracteristici

This book is open access, which means that you have free and unlimited access