Journal ArticleDOI
Segmentation of the multimodal brain tumor image used the multi-pathway architecture method based on 3D FCN
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TLDR
A novel model based on 3D fully convolutional network is proposed that applies multi-pathway architecture to feature extraction so as to effectively extract features from multi-modal MRI images.About:
This article is published in Neurocomputing.The article was published on 2021-01-29. It has received 70 citations till now. The article focuses on the topics: Segmentation & Feature (computer vision).read more
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Journal ArticleDOI
Deep Learning in Cancer Diagnosis and Prognosis Prediction: A Minireview on Challenges, Recent Trends, and Future Directions.
Ahsan Bin Tufail,Ahsan Bin Tufail,Yong-Kui Ma,Mohammed K. A. Kaabar,Francisco Martínez,A R Junejo,Inam Ullah,Rahim Khan +7 more
TL;DR: Deep learning (DL) is a branch of machine learning and artificial intelligence that has been applied to many areas in different domains such as health care and drug design as mentioned in this paper, which has emerged as a technology of choice due to the availability of high computational resources.
Journal ArticleDOI
A Survey of Brain Tumor Segmentation and Classification Algorithms.
Erena Siyoum Biratu,Friedhelm Schwenker,Yehualashet Megersa Ayano,Taye Girma Debelee,Taye Girma Debelee +4 more
TL;DR: A comprehensive survey of three, recently proposed, major brain tumor segmentation and classification model techniques, namely, region growing, shallow machine learning and deep learning, can be found in this paper.
Journal ArticleDOI
Brain tumor segmentation of MRI images: A comprehensive review on the application of artificial intelligence tools
Ramin Ranjbarzadeh,Antonella Caputo,Erfan Babaee Tirkolaee,Saeid Jafarzadeh Ghoushchi,Malika Bendechache +4 more
TL;DR: A comprehensive review of the recent Artificial Intelligence (AI) methods of diagnosing brain tumors using MRI images is presented in this article , which can be divided into Supervised, Unsupervised, and Deep Learning (DL) methods.
Journal ArticleDOI
Multimodal medical image segmentation using multi-scale context-aware network
TL;DR: Wang et al. as discussed by the authors proposed a multi-scale context-aware network (CA-Net) for multimodal medical image segmentation, which captures rich context information with dense skip connection and assigns distinct weights to different channels.
Journal ArticleDOI
Multimodal Medical Image Segmentation Using Multi-scale Context-aware Network
TL;DR: Wang et al. as mentioned in this paper proposed a multi-scale context-aware network (CA-Net) for multimodal medical image segmentation, which captures rich context information with dense skip connection and assigns distinct weights to different channels.
References
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Proceedings Article
ImageNet Classification with Deep Convolutional Neural Networks
TL;DR: The state-of-the-art performance of CNNs was achieved by Deep Convolutional Neural Networks (DCNNs) as discussed by the authors, which consists of five convolutional layers, some of which are followed by max-pooling layers, and three fully-connected layers with a final 1000-way softmax.
Book ChapterDOI
U-Net: Convolutional Networks for Biomedical Image Segmentation
TL;DR: Neber et al. as discussed by the authors proposed a network and training strategy that relies on the strong use of data augmentation to use the available annotated samples more efficiently, which can be trained end-to-end from very few images and outperforms the prior best method (a sliding-window convolutional network) on the ISBI challenge for segmentation of neuronal structures in electron microscopic stacks.
Journal ArticleDOI
Gradient-based learning applied to document recognition
Yann LeCun,Léon Bottou,Léon Bottou,Yoshua Bengio,Yoshua Bengio,Yoshua Bengio,Patrick Haffner +6 more
TL;DR: In this article, a graph transformer network (GTN) is proposed for handwritten character recognition, which can be used to synthesize a complex decision surface that can classify high-dimensional patterns, such as handwritten characters.
Proceedings ArticleDOI
Fully convolutional networks for semantic segmentation
TL;DR: The key insight is to build “fully convolutional” networks that take input of arbitrary size and produce correspondingly-sized output with efficient inference and learning.
Posted Content
U-Net: Convolutional Networks for Biomedical Image Segmentation
TL;DR: It is shown that such a network can be trained end-to-end from very few images and outperforms the prior best method (a sliding-window convolutional network) on the ISBI challenge for segmentation of neuronal structures in electron microscopic stacks.
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