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
PnP-AdaNet: Plug-and-Play Adversarial Domain Adaptation Network at Unpaired Cross-Modality Cardiac Segmentation
TL;DR: A plug-and-play adversarial domain adaptation network (PnP-AdaNet) for adapting segmentation networks between different modalities of medical images, e.g., MRI and CT, which outperforms many state-of-the-art unsupervised domain adaptation approaches on the same dataset.
PatentDOI
3D segmentation with exponential logarithmic loss for highly unbalanced object sizes
TL;DR: In this paper, a 3D segmentation with exponential logarithmic loss for highly unbalanced object sizes is provided, where an artificial neural network is trained to label an anatomical feature in medical images.
Journal ArticleDOI
Full left ventricle quantification via deep multitask relationships learning.
TL;DR: A deep multitask relationship learning network (DMTRL) that first obtains expressive and robust cardiac representations with a deep convolution neural network, then models the temporal dynamics of cardiac sequences effectively with two parallel recurrent neural network (RNN) modules, and estimates the cardiac phase with a softmax classifier.
Journal ArticleDOI
Semi-supervised adversarial model for benign–malignant lung nodule classification on chest CT
TL;DR: A semi-supervised adversarial classification (SSAC) model that can be trained by using both labeled and unlabeled data for benign-malignant lung nodule classification is proposed and achieves superior performance on the benchmark LIDC-IDRI dataset.
Proceedings ArticleDOI
A 3D Coarse-to-Fine Framework for Volumetric Medical Image Segmentation
TL;DR: In this article, a 3D-based coarse-to-fine framework was proposed to segment volumetric medical images, which outperforms the 2D counterpart to a large margin since it can leverage the rich spatial information along all three axes.
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.