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 Article
Medical Image Segmentation Based on GA Optimized BP Neural Network
TL;DR: GA-BP is adopted in medical image segmentation, training genetic neural network and putting sample value and feature value extracted into the genetic Neural network for training.
Journal ArticleDOI
Attention-Assisted Adversarial Model for Cerebrovascular Segmentation in 3D TOF-MRA Volumes
TL;DR: Experimental results indicate that A-SegAN achieves competitive or better cerebrovascular segmentation results compared to other deep learning methods, effectively alleviating the above issues.
Journal ArticleDOI
Whole Heart Segmentation Using 3D FM-Pre-ResNet Encoder–Decoder Based Architecture with Variational Autoencoder Regularization
TL;DR: A novel connectivity structure of residual unit that is referred to as a feature merge residual unit (FM-Pre-ResNet) is introduced that allows the creation of distinctly deep models without an increase in the number of parameters compared to the pre-activation residual units.
Journal ArticleDOI
Prediction of major torso organs in low-contrast micro-CT images of mice using a two-stage deeply supervised fully convolutional network
TL;DR: The accuracy and efficiency of mouse micro-CT image analysis are greatly improved and the need for using contrast agent and high X-ray dose is potentially reduced and that localized single organ prediction is more accurate than global multiple organ prediction.
Journal ArticleDOI
A Review of Artificial Intelligence's Neural Networks (Deep Learning) Applications in Medical Diagnosis and Prediction
TL;DR: In this article, a review of deep learning applications in medical diagnosis and prediction, such as Convolutional Neural Networks (CNN), Fully convolutional Networks (FCN), and Generative Adversarial Networks (GANs), is presented.
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.