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
LinSEM: Linearizing segmentation evaluation metrics for medical images
TL;DR: A method called LinSEM for linearizing commonly used segmentation evaluation metrics based on corresponding degrees of acceptability evaluated by an expert in a reader study, which achieves significantly improved uniformity of meaning post-linearization across all tested objects and metrics.
Posted Content
Cross-Classification Clustering: An Efficient Multi-Object Tracking Technique for 3-D Instance Segmentation in Connectomics
TL;DR: This work introduces cross-classification clustering (3C), a technique that simultaneously tracks complex, interrelated objects in an image stack and applies the mechanism to achieve state-of-the-art accuracy in connectomics -- the nanoscale mapping of neural tissue from electron microscopy volumes.
Journal ArticleDOI
Aortic wall segmentation in 18F-sodium fluoride PET/CT scans: Head-to-head comparison of artificial intelligence-based versus manual segmentation.
Reza Piri,Lars Edenbrandt,Lars Edenbrandt,Måns Larsson,Olof Enqvist,Amalie Horstmann Nøddeskou-Fink,Oke Gerke,Oke Gerke,Poul Flemming Høilund-Carlsen,Poul Flemming Høilund-Carlsen +9 more
TL;DR: In this article, an automated CNN-based method for rapid segmentation of the aortic wall in positron emission tomography/computed tomography (PET/CT) scans is presented.
Journal ArticleDOI
FCN Based Label Correction for Multi-Atlas Guided Organ Segmentation.
TL;DR: This paper proposes a fully convolutional network with residual connections to learn the relationship between the image patch pair and the related label confidence patch, and develops a deep learning-based confidence estimation method to alleviate the potential effects of registration errors.
Journal ArticleDOI
A Mutually Attentive Co-Training Framework for Semi-Supervised Recognition
TL;DR: A novel Mutually Attentive Co-training Framework (MACF) is proposed that can effectively alleviate the negative impacts of incorrect labels on model retraining by exploring deep model disagreements and improving the pseudo labels by aggregating the predictions from multi-models and data transformations.
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
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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.