Journal ArticleDOI
Discriminative ensemble learning for few-shot chest x-ray diagnosis.
TLDR
The proposed method for few-shot diagnosis of diseases and conditions from chest x-rays using discriminative ensemble learning is modular and easily adaptable to new tasks requiring the training of only the saliency-based classifier.About:
This article is published in Medical Image Analysis.The article was published on 2021-02-01. It has received 29 citations till now. The article focuses on the topics: Ensemble learning & Autoencoder.read more
Citations
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Journal ArticleDOI
Deep learning for chest X-ray analysis: A survey.
TL;DR: In this article, a review of deep learning on chest X-ray images is presented, focusing on image-level prediction (classification and regression), segmentation, localization, image generation and domain adaptation.
Journal ArticleDOI
Self-Supervised Generalized Zero Shot Learning for Medical Image Classification Using Novel Interpretable Saliency Maps
TL;DR: A GZSL method that uses self supervised learning for selecting representative vectors of disease classes; and synthesizing features of unseen classes is proposed, and a novel approach to generate GradCAM saliency maps that highlight diseased regions with greater accuracy is proposed.
Journal ArticleDOI
Clinical Artificial Intelligence Applications in Radiology: Chest and Abdomen.
Sungwon Lee,Ronald M. Summers +1 more
TL;DR: In this paper, the authors introduce the current published articles of AI applied to chest and abdominal radiology, including organ segmentation, lesion detection, classification, and predicting prognosis.
Journal ArticleDOI
Disease Localization and Severity Assessment in Chest X-Ray Images using Multi-Stage Superpixels Classification
TL;DR: In this article , a multistage superpixel classification-based disease localization and severity assessment framework is proposed to generate a compact disease boundary, infection map, and grade the infection severity.
Journal ArticleDOI
Artificial Intelligence in Medical Imaging and its Impact on the Rare Disease Community: Threats, Challenges and Opportunities
TL;DR: In this paper , the authors proposed an Artificial Intelligence (AI)-based positron emission tomography (PET) for the diagnosis of rare diseases (RDs) using patient advocacy groups.
References
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Random Forests
TL;DR: Internal estimates monitor error, strength, and correlation and these are used to show the response to increasing the number of features used in the forest, and are also applicable to regression.
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 ArticleDOI
ImageNet: A large-scale hierarchical image database
TL;DR: A new database called “ImageNet” is introduced, a large-scale ontology of images built upon the backbone of the WordNet structure, much larger in scale and diversity and much more accurate than the current image datasets.
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Deep learning
TL;DR: Deep learning is making major advances in solving problems that have resisted the best attempts of the artificial intelligence community for many years, and will have many more successes in the near future because it requires very little engineering by hand and can easily take advantage of increases in the amount of available computation and data.
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.