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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Peer Review
A c omprehensive r eview on d eep s upervision : t heories and a pplications
Renjie Li,Xinyi Wang +1 more
TL;DR: A comprehensive in-depth review of deep supervision in both theories and applications is provided, which proposes a new classification of different deep supervision networks, and discusses advantages and limitations of currentDeep supervision networks in computer vision applications.
Capturing implicit hierarchical structure in 3D biomedical images with self-supervised hyperbolic representations
TL;DR: This work considers encoder-decoder architectures with a hyperbolic latent space, to explicitly capture hierarchical relationships present in subvolumes of the data, and introduces an essential self-supervised loss—in addition to the standard VAE loss—which infers approximate hierarchies.
Proceedings ArticleDOI
Medical Image Classification using Deep Learning Techniques: A Review
TL;DR: Deep learning techniques have been turned out to be one of the successful and most promising techniques used for medical image processing as discussed by the authors , which is the best in finding classification problems in medical imaging processing.
Proceedings ArticleDOI
GI Tract Image Segmentation with U-Net 3D
TL;DR: In this article , the authors used Unet3D for stomach and intestines segmentation, which could assist radiation oncologists to deliver high doses of radiation using X-ray beams pointed to tumors while avoiding the stomach.
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.