Journal ArticleDOI
3D deeply supervised network for automated segmentation of volumetric medical images.
TLDR
The proposed 3D DSN is capable of conducting volume‐to‐volume learning and inference, which can eliminate redundant computations and alleviate the risk of over‐fitting on limited training data, and the3D deep supervision mechanism can effectively cope with the optimization problem of gradients vanishing or exploding when training a 3D deep model.About:
This article is published in Medical Image Analysis.The article was published on 2017-10-01. It has received 507 citations till now. The article focuses on the topics: Scale-space segmentation & Image segmentation.read more
Citations
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Journal ArticleDOI
MDCF_Net: A multi-dimensional hybrid network for liver and tumor segmentation from CT
TL;DR: Wang et al. as discussed by the authors proposed MDCF_Net, which has dual encoding branches composed of CNN and Cnn-former and can fully utilize multi-dimensional image features for liver cancer segmentation.
Posted Content
Multi-Modality Cardiac Image Analysis with Deep Learning.
TL;DR: In this paper, two benchmark works for multi-sequence cardiac MRI based myocardial and pathology segmentation were presented, and two novel frameworks for left atrial scar segmentation and quantification from LGE MRI were presented.
Journal ArticleDOI
Lossless segmentation of cardiac medical images by a resolution consistent network with nondamage data preprocessing
Posted Content
Unsupervised Domain Adaptation with Variational Approximation for Cardiac Segmentation
Fuping Wu,Xiahai Zhuang +1 more
TL;DR: In this article, the latent features of both domains are driven towards a common and parameterized variational form, whose conditional distribution given the image is Gaussian, which is achieved by two networks based on variational auto-encoders (VAEs) and a regularization for this variational approximation.
Book ChapterDOI
Efficient 3D Deep Learning for Myocardial Diseases Segmentation
TL;DR: In this article, a 3D U-Net based on Shape prior to identifying myocardial infarction and myocardium ventricular obstruction (MVO) segmentations from the encoder-decoder prediction was proposed.
References
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Proceedings ArticleDOI
Deep Residual Learning for Image Recognition
TL;DR: In this article, the authors proposed a residual learning framework to ease the training of networks that are substantially deeper than those used previously, which won the 1st place on the ILSVRC 2015 classification task.
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.
Proceedings Article
Very Deep Convolutional Networks for Large-Scale Image Recognition
Karen Simonyan,Andrew Zisserman +1 more
TL;DR: This work investigates the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting using an architecture with very small convolution filters, which shows that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 weight layers.
Proceedings Article
Very Deep Convolutional Networks for Large-Scale Image Recognition
Karen Simonyan,Andrew Zisserman +1 more
TL;DR: In this paper, the authors investigated the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting and showed that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 layers.
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.