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
Ultrasound prostate segmentation based on multidirectional deeply supervised V-Net
Yang Lei,Sibo Tian,Xiuxiu He,Tonghe Wang,Bo Wang,Pretesh Patel,Ashesh B. Jani,Hui Mao,Walter J. Curran,Tian Liu,Xiaofeng Yang +10 more
TL;DR: A novel deeply supervised deep learning-based approach with reliable contour refinement to automatically segment the TRUS prostate is developed, demonstrated its clinical feasibility, and validated its accuracy compared to manual segmentation.
Journal ArticleDOI
Deep Attentive Features for Prostate Segmentation in 3D Transrectal Ultrasound
Yi Wang,Dong Ni,Haoran Dou,Xiaowei Hu,Lei Zhu,Xin Yang,Ming Xu,Jing Qin,Pheng-Ann Heng,Tianfu Wang +9 more
TL;DR: Wu et al. as discussed by the authors developed a 3D deep neural network equipped with attention modules for better prostate segmentation in transrectal ultrasound (TRUS) images by fully exploiting the complementary information encoded in different layers of the convolutional neural network.
Proceedings ArticleDOI
ToothNet: Automatic Tooth Instance Segmentation and Identification From Cone Beam CT Images
TL;DR: This method is the first to use neural networks to achieve automatic tooth segmentation and identification from CBCT images and encodes teeth spatial relationships as an additional feature input in the identification task, which helps to remarkably improve the identification accuracy.
Book ChapterDOI
CIA-Net: Robust Nuclei Instance Segmentation with Contour-Aware Information Aggregation
TL;DR: A novel deep neural network, namely Contour-aware Informative Aggregation Network (CIA-Net) with multi-level information aggregation module between two task-specific decoders that leverages the merit of spatial and texture dependencies between nuclei and contour by bi-directionally aggregating task- specific features.
Journal ArticleDOI
Weakly supervised 3D deep learning for breast cancer classification and localization of the lesions in MR images
TL;DR: The usefulness of 3D deep learning‐based classification of breast cancer and malignancy localization from MRI has been reported and can potentially be very useful in the clinical domain and aid radiologists in breast cancer diagnosis.
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.