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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Patent
Training deep learning engines for radiotherapy treatment planning
TL;DR: In this paper, a set of training data that includes unlabeled training data and labeled training data is used to train a deep learning engine to perform radiotherapy treatment planning task by processing training data instance to generate primary output data and deep supervision output data.
Proceedings ArticleDOI
Unsupervised Domain Adaptation for Cardiac Segmentation: Towards Structure Mutual Information Maximization
TL;DR: In this article , a Structure Mutual Information Estimation (SMIE) block with a global estimator, a local estimator and a prior information matching estimator is proposed to maximize the mutual information between the reconstruction and segmentation tasks.
Journal ArticleDOI
RE-3DLVNet: Refined estimation of the left ventricle volume via interactive 3D segmentation and reinforced quantification
Rongjun Ge,Cong Xia,Yuting He,Hailong Sun,Daoqiang Zhang,Guanyu Yang,Wentao Xiang,Limin Luo,Yinsu Zhu,Shuo Li,YangQuan Chen +10 more
TL;DR: The authors-3DLVNet The authors proposes a reinforced quantification to refine the reinforced volume of the segmentation, with focused contracted output space, and a consistent constraint transfers the ReinQuan deviation between segmented result and its ground truth for penetratively penalizing.
Journal ArticleDOI
Can AI Automatically Assess Scan Quality of Hip Ultrasound?
Abhilash Rakkunedeth Hareendrananthan,Myles Mabee,Baljot S. Chahal,Sukhdeep Dulai,Jacob L. Jaremko +4 more
TL;DR: Using the AI-based approach as a screening tool during ultrasound scanning or postprocessing would ensure high scan quality and lead to more reliable ultrasound hip examination in infants.
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.