- Location
-
Deutsche Nationalbibliothek Frankfurt am Main
- ISSN
-
1432-2315
- Extent
-
Online-Ressource
- Language
-
Englisch
- Notes
-
online resource.
- Bibliographic citation
-
Cell image segmentation by using feedback and convolutional LSTM ; day:4 ; month:7 ; year:2021 ; pages:1-11
The visual computer ; (4.7.2021), 1-11
- Creator
-
Shibuya, Eisuke
Hotta, Kazuhiro
- Contributor
-
SpringerLink (Online service)
- DOI
-
10.1007/s00371-021-02221-3
- URN
-
urn:nbn:de:101:1-2021101418560705507677
- Rights
-
Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Last update
-
15.08.2025, 7:35 AM CEST
Data provider
This object is provided by:
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Associated
- Shibuya, Eisuke
- Hotta, Kazuhiro
- SpringerLink (Online service)
Other Objects (12)
Fully Convolutional Networks for Image Segmentation
Multi-level image segmentation with internal feedback mechanisms
Multimodal Biomedical Image Segmentation using Multi-Dimensional U-Convolutional Neural Network
A framework for feedback-based segmentation of 3D image stacks
Stochastic gradient descent optimisation for convolutional neural network for medical image segmentation
Hybrid Convolutional and Graph-Convolutional Framework for Segmentation Refinement
Maximal sparse convex surrogate-assisted evolutionary convolutional neural architecture search for image segmentation
Investigating Semantic Augmentation in Virtual Environments for Image Segmentation Using Convolutional Neural Networks
Multiresolution image segmentation
Large scale tissue histopathology image classification, segmentation, and visualization via deep convolutional activation features
RESIDUAL SHUFFLING CONVOLUTIONAL NEURAL NETWORKS FOR DEEP SEMANTIC IMAGE SEGMENTATION USING MULTI-MODAL DATA
Segmentation of MRI Brain Tumor Image using Optimization based Deep Convolutional Neural networks (DCNN)
Fully Convolutional Networks for Image Segmentation
Multi-level image segmentation with internal feedback mechanisms
Multimodal Biomedical Image Segmentation using Multi-Dimensional U-Convolutional Neural Network
A framework for feedback-based segmentation of 3D image stacks
Stochastic gradient descent optimisation for convolutional neural network for medical image segmentation
Hybrid Convolutional and Graph-Convolutional Framework for Segmentation Refinement
Maximal sparse convex surrogate-assisted evolutionary convolutional neural architecture search for image segmentation
Investigating Semantic Augmentation in Virtual Environments for Image Segmentation Using Convolutional Neural Networks
Multiresolution image segmentation
Large scale tissue histopathology image classification, segmentation, and visualization via deep convolutional activation features
RESIDUAL SHUFFLING CONVOLUTIONAL NEURAL NETWORKS FOR DEEP SEMANTIC IMAGE SEGMENTATION USING MULTI-MODAL DATA
Segmentation of MRI Brain Tumor Image using Optimization based Deep Convolutional Neural networks (DCNN)
Fully Convolutional Networks for Image Segmentation
Multi-level image segmentation with internal feedback mechanisms
Multimodal Biomedical Image Segmentation using Multi-Dimensional U-Convolutional Neural Network
A framework for feedback-based segmentation of 3D image stacks
Stochastic gradient descent optimisation for convolutional neural network for medical image segmentation
Hybrid Convolutional and Graph-Convolutional Framework for Segmentation Refinement
Maximal sparse convex surrogate-assisted evolutionary convolutional neural architecture search for image segmentation
Investigating Semantic Augmentation in Virtual Environments for Image Segmentation Using Convolutional Neural Networks
Multiresolution image segmentation
Large scale tissue histopathology image classification, segmentation, and visualization via deep convolutional activation features
RESIDUAL SHUFFLING CONVOLUTIONAL NEURAL NETWORKS FOR DEEP SEMANTIC IMAGE SEGMENTATION USING MULTI-MODAL DATA