Efficient Multi-Scale 3D CNN with Fully Connected CRF for Accurate Brain Lesion Segmentation
Konstantinos Kamnitsas,Christian Ledig,Virginia F. J. Newcombe,Joanna P. Simpson,Andrew D. Kane,David K. Menon,Daniel Rueckert,Ben Glocker +7 more
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TLDR
An efficient and effective dense training scheme which joins the processing of adjacent image patches into one pass through the network while automatically adapting to the inherent class imbalance present in the data, and improves on the state-of-the‐art for all three applications.About:
This article is published in Medical Image Analysis.The article was published on 2017-02-01 and is currently open access. It has received 2842 citations till now.read more
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Journal ArticleDOI
A survey on deep learning in medical image analysis
Geert Litjens,Thijs Kooi,Babak Ehteshami Bejnordi,Arnaud Arindra Adiyoso Setio,Francesco Ciompi,Mohsen Ghafoorian,Jeroen van der Laak,Bram van Ginneken,Clara I. Sánchez +8 more
TL;DR: This paper reviews the major deep learning concepts pertinent to medical image analysis and summarizes over 300 contributions to the field, most of which appeared in the last year, to survey the use of deep learning for image classification, object detection, segmentation, registration, and other tasks.
Proceedings ArticleDOI
V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation
TL;DR: In this article, a volumetric, fully convolutional neural network (FCN) was proposed to predict segmentation for the whole volume at one time, which can deal with situations where there is a strong imbalance between the number of foreground and background voxels.
Posted Content
Attention U-Net: Learning Where to Look for the Pancreas
Ozan Oktay,Jo Schlemper,Loic Le Folgoc,Matthew C. H. Lee,Mattias P. Heinrich,Kazunari Misawa,Kensaku Mori,Steven McDonagh,Nils Y. Hammerla,Bernhard Kainz,Ben Glocker,Daniel Rueckert +11 more
TL;DR: A novel attention gate (AG) model for medical imaging that automatically learns to focus on target structures of varying shapes and sizes is proposed to eliminate the necessity of using explicit external tissue/organ localisation modules of cascaded convolutional neural networks (CNNs).
Posted Content
V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation
TL;DR: This work proposes an approach to 3D image segmentation based on a volumetric, fully convolutional, neural network, trained end-to-end on MRI volumes depicting prostate, and learns to predict segmentation for the whole volume at once.
Journal ArticleDOI
Artificial intelligence in healthcare: past, present and future
Fei Jiang,Yong Jiang,Hui Zhi,Yi Dong,Hao Li,Sufeng Ma,Yilong Wang,Qiang Dong,Haipeng Shen,Yongjun Wang +9 more
TL;DR: The current status of AI applications in healthcare, in the three major areas of early detection and diagnosis, treatment, as well as outcome prediction and prognosis evaluation, are surveyed and its future is discussed.
References
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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.
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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.
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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.
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Gradient-based learning applied to document recognition
Yann LeCun,Léon Bottou,Léon Bottou,Yoshua Bengio,Yoshua Bengio,Yoshua Bengio,Patrick Haffner +6 more
TL;DR: In this article, a graph transformer network (GTN) is proposed for handwritten character recognition, which can be used to synthesize a complex decision surface that can classify high-dimensional patterns, such as handwritten characters.