Generative adversarial network in medical imaging: A review.
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TLDR
A review of recent advances in medical imaging using the adversarial training scheme with the hope of benefiting researchers interested in this technique.About:
This article is published in Medical Image Analysis.The article was published on 2019-12-01 and is currently open access. It has received 1053 citations till now. The article focuses on the topics: Generative model.read more
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Book ChapterDOI
Cross Attention Transformers for Multi-modal Unsupervised Whole-Body PET Anomaly Detection
Ashay Patel,Petru-Daniel Tudosiu,Walter H. L. Pinaya,Gary Cook,Vicky Goh,Sebastien Ourselin,M. Jorge Cardoso +6 more
TL;DR: In this paper , a multi-modal conditioning of the transformer via cross-attention was introduced to aid the PET anomaly detection task, and the proposed model uncertainty, in conjunction with a kernel density estimation approach, was shown to provide a statistically robust alternative to residual-based anomaly maps.
Journal ArticleDOI
Endocrine Tumor Classification via Machine-Learning-Based Elastography: A Systematic Scoping Review
Ye-Jiao Mao,Li-Wen Zha,Andy Yiu-Chau Tam,Hyo-Jung Lim,A. K. Cheung,Ying-Qi Zhang,Ming Ni,James Chung Wai Cheung,Duo Wai-Chi Wong +8 more
TL;DR: In this paper , the authors focused on the applications and performance of machine-learning-based elastography in classifying endocrine tumors, which can further improve diagnostic accuracy and reliability.
Journal ArticleDOI
MEDAS: an open-source platform as a service to help break the walls between medicine and informatics
TL;DR: Wang et al. as discussed by the authors proposed MEDAS, an open-source platform providing collaborative and interactive services for researchers from a medical background using DL-related toolkits easily and for scientists or engineers from informatics modeling faster.
Journal ArticleDOI
Concerns in the use of adversarial learning for image synthesis in cardiovascular intervention
Journal ArticleDOI
Convolutional Neural Network Based Frameworks for Fast Automatic Segmentation of Thalamic Nuclei from Native and Synthesized Contrast Structural MRI.
Lavanya Umapathy,Mahesh Bharath Keerthivasan,Mahesh Bharath Keerthivasan,Natalie M. Zahr,Ali Bilgin,Manojkumar Saranathan +5 more
TL;DR: In this paper, a 3D convolutional neural network (CNN) based framework for thalamic nuclei parcellation using T1-weighted Magnetization Prepared Rapid Gradient Echo (MPRAGE) images was proposed.
References
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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.
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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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Image quality assessment: from error visibility to structural similarity
TL;DR: In this article, a structural similarity index is proposed for image quality assessment based on the degradation of structural information, which can be applied to both subjective ratings and objective methods on a database of images compressed with JPEG and JPEG2000.
Journal ArticleDOI
Generative Adversarial Nets
Ian Goodfellow,Jean Pouget-Abadie,Mehdi Mirza,Bing Xu,David Warde-Farley,Sherjil Ozair,Aaron Courville,Yoshua Bengio +7 more
TL;DR: A new framework for estimating generative models via an adversarial process, in which two models are simultaneously train: a generative model G that captures the data distribution and a discriminative model D that estimates the probability that a sample came from the training data rather than G.