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
DDeep3M+: adaptive enhancement powered weakly supervised learning for neuron segmentation
TL;DR: In this article , a Hessian analysis based adaptive enhancement filter is employed to generate pseudo-labels for segmenting neuron images, and the automated segmentation labels are input for training a DDeep3M to extract neuronal features.
Proceedings ArticleDOI
A Study of Deep Learning based Techniques for the Detection of Maize Leaf Disease: A Short Review
TL;DR: An overview of deep learning methods for identifying plant diseases is given in this article , which also includes data collection sources, deep learning architectures, and image processing methods, with a focus on future research to improve system performance and accuracy for detecting crop diseases using better deep learning capabilities.
Journal ArticleDOI
The Added Value of 3D Imaging and 3D Printing in Head and Neck Surgeries
Evgenia Parioti,Stavros Pitoglou,Arianna Filntisi,Athanasios Anastasiou,Ourania Petropoulou,Dimitris Koutsouris +5 more
TL;DR: This review aims to evaluate the use of 3D imaging and 3D printing techniques in head and neck surgery and concludes that these technologies have revolutionized medicine.
Proceedings ArticleDOI
Deep Active Learning for Cardiac Image Segmentation
TL;DR: The results of the experiment show that the active learning method proposed is obviously better than the random sampling method, and only a small amount of labeled data is needed to achieve the segmentation results achieved by training the model with all data sets.
Journal ArticleDOI
3D Vessel Segmentation with Limited Guidance of 2D Structure-agnostic Vessel Annotations
Huai Chen,Xiu Wang,Lisheng Wang +2 more
TL;DR: Wang et al. as mentioned in this paper proposed a 3D shape-guided local discrimination model for 3D vascular segmentation under limited guidance from public 2D vessel annotations, where 3D vessels are composed of semantically similar voxels and exhibit tree-shaped morphology.
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.