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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Posted ContentDOI
Brain Tumor Segmentation Based on Region of Interest-Aided Localization and Segmentation U-Net
TL;DR: Signs are indications that the proposed method is an effective ROI-aided deep learning strategy for brain MRI tumor segmentation, and a valid and useful tool in medical image processing.
Book ChapterDOI
Spatial and Channel Attention Modulated Network for Medical Image Segmentation
Wenhao Fang,Xian-Hua Han +1 more
TL;DR: Wang et al. as discussed by the authors propose a novel attention modulated network based on the baseline U-Net, and explores embedded spatial and channel attention modules for adaptively highlighting interdependent channel maps and focusing on more discriminant regions via investigating relevant feature association.
Posted Content
A Teacher-Student Framework for Semi-supervised Medical Image Segmentation From Mixed Supervision.
TL;DR: A semi-supervised learning framework based on a teacher-student fashion for organ and lesion segmentation with partial dense-labeled supervision and supplementary loose bounding-box supervision which are easier to acquire is developed.
Journal ArticleDOI
Deep learning-based segmentation of malignant pleural mesothelioma tumor on computed tomography scans: application to scans demonstrating pleural effusion.
TL;DR: Deep convolutional neural networks of the U-Net architecture were trained for segmentation of tumor in the left and right hemithoraces and achieved significantly higher performance on both test sets when compared with a previous deep learning-based segmentation method for mesothelioma.
Posted Content
Neural Style Transfer Improves 3D Cardiovascular MR Image Segmentation on Inconsistent Data
TL;DR: StyleSegor, an efficient and easy-to-use strategy to alleviate the inconsistency in medical images generated by different machines and hospitals, and is corroborated to be an accurate tool for 3D whole heart segmentation especially on highly inconsistent data.
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.