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
Projection-domain scatter correction for cone beam computed tomography using a residual convolutional neural network.
TL;DR: The proposed deep learning-based method provides an effective tool for CBCT scatter correction and holds significant value for quantitative imaging and image-guided radiation therapy.
Proceedings ArticleDOI
Automatic Segmentation of Kidney and Renal Tumor in CT Images Based on 3D Fully Convolutional Neural Network with Pyramid Pooling Module
Guanyu Yang,Guoqing Li,Tan Pan,Youyong Kong,Jiasong Wu,Huazhong Shu,Limin Luo,Jean-Louis Dillenseger,Jean-Louis Coatrieux,Lijun Tang,Xiaomei Zhu +10 more
TL;DR: This paper proposes a new method to perform a precise segmentation of kidney and renal tumor in CT angiography images which relies on a three-dimensional (3D) fully convolutional network (FCN) which combines a pyramid pooling module (PPM).
Journal ArticleDOI
Focus U-Net: A novel dual attention-gated CNN for polyp segmentation during colonoscopy.
TL;DR: Focus U-Net as mentioned in this paper is a dual attention-gated deep neural network, which combines efficient spatial and channel-based attention into a single Focus Gate module to encourage selective learning of polyp features.
Proceedings ArticleDOI
Every Annotation Counts: Multi-label Deep Supervision for Medical Image Segmentation
TL;DR: In this article, a semi-weakly supervised segmentation algorithm is proposed for retinal fluid segmentation based on a new formulation of deep supervision and student-teacher model and allows for easy integration of different supervision signals.
Book ChapterDOI
Class-Balanced Deep Neural Network for Automatic Ventricular Structure Segmentation
TL;DR: This paper proposes a general and fully automatic solution to concurrently segment three important ventricular structures from cardiovascular MR scan and investigates the capacity of different loss functions and proposes a Multi-class Dice Similarity Coefficient (mDSC) based loss function to re-weight the training for all classes.
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.