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Author

Michal Amitai

Other affiliations: Tel Aviv University
Bio: Michal Amitai is an academic researcher from Sheba Medical Center. The author has contributed to research in topics: Crohn's disease & Capsule endoscopy. The author has an hindex of 19, co-authored 67 publications receiving 2518 citations. Previous affiliations of Michal Amitai include Tel Aviv University.


Papers
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Journal ArticleDOI
TL;DR: It is shown that generated medical images can be used for synthetic data augmentation, and improve the performance of CNN for medical image classification, and generalize to other medical classification applications and thus support radiologists’ efforts to improve diagnosis.

1,202 citations

Proceedings ArticleDOI
04 Apr 2018
TL;DR: In this article, a data augmentation method that generates synthetic medical images using Generative Adversarial Networks (GANs) is presented, which is demonstrated on a limited dataset of computed tomography (CT) images of 182 liver lesions.
Abstract: In this paper, we present a data augmentation method that generates synthetic medical images using Generative Adversarial Networks (GANs). We propose a training scheme that first uses classical data augmentation to enlarge the training set and then further enlarges the data size and its diversity by applying GAN techniques for synthetic data augmentation. Our method is demonstrated on a limited dataset of computed tomography (CT) images of 182 liver lesions (53 cysts, 64 metastases and 65 hemangiomas). The classification performance using only classic data augmentation yielded 78.6% sensitivity and 88.4% specificity. By adding the synthetic data augmentation the results significantly increased to 85.7% sensitivity and 92.4% specificity.

569 citations

Journal ArticleDOI
TL;DR: An introduction to deep learning technology is provided and the stages that are entailed in the design process of deep learning radiology research are presented and the results of a survey of the application of convolutional neural networks to radiologic imaging are detailed.
Abstract: This article is a guide to convolutional neural network technologies and their clinical applications in the analysis of radiologic images.

287 citations

Journal ArticleDOI
TL;DR: Intussusception on CT presented a characteristic mass lesion containing fat stripes in almost all patients, the most common cause of prolonged abdominal pain, and therefore early diagnosis and prompt intervention are essential.

192 citations

Journal ArticleDOI
TL;DR: A fully convolutional network (FCN) with a conditional generative adversarial network (GAN) to generate simulated PET data from given input CT data to enable lesion detection and drug treatment evaluation in a CT-only environment, thus reducing the need for the more expensive and radioactive PET/CT scan.

126 citations


Cited by
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Journal ArticleDOI
TL;DR: This survey will present existing methods for Data Augmentation, promising developments, and meta-level decisions for implementing DataAugmentation, a data-space solution to the problem of limited data.
Abstract: Deep convolutional neural networks have performed remarkably well on many Computer Vision tasks. However, these networks are heavily reliant on big data to avoid overfitting. Overfitting refers to the phenomenon when a network learns a function with very high variance such as to perfectly model the training data. Unfortunately, many application domains do not have access to big data, such as medical image analysis. This survey focuses on Data Augmentation, a data-space solution to the problem of limited data. Data Augmentation encompasses a suite of techniques that enhance the size and quality of training datasets such that better Deep Learning models can be built using them. The image augmentation algorithms discussed in this survey include geometric transformations, color space augmentations, kernel filters, mixing images, random erasing, feature space augmentation, adversarial training, generative adversarial networks, neural style transfer, and meta-learning. The application of augmentation methods based on GANs are heavily covered in this survey. In addition to augmentation techniques, this paper will briefly discuss other characteristics of Data Augmentation such as test-time augmentation, resolution impact, final dataset size, and curriculum learning. This survey will present existing methods for Data Augmentation, promising developments, and meta-level decisions for implementing Data Augmentation. Readers will understand how Data Augmentation can improve the performance of their models and expand limited datasets to take advantage of the capabilities of big data.

5,782 citations

Reference EntryDOI
15 Oct 2004

2,118 citations

Journal ArticleDOI
TL;DR: It is shown that generated medical images can be used for synthetic data augmentation, and improve the performance of CNN for medical image classification, and generalize to other medical classification applications and thus support radiologists’ efforts to improve diagnosis.

1,202 citations

Journal ArticleDOI
TL;DR: A review of recent advances in medical imaging using the adversarial training scheme with the hope of benefiting researchers interested in this technique.

1,053 citations

Journal ArticleDOI
TL;DR: In this article, the authors provide a short overview of recent advances and some associated challenges in machine learning applied to medical image processing and image analysis, and provide a starting point for people interested in experimenting and perhaps contributing to the field of machine learning for medical imaging.
Abstract: What has happened in machine learning lately, and what does it mean for the future of medical image analysis? Machine learning has witnessed a tremendous amount of attention over the last few years. The current boom started around 2009 when so-called deep artificial neural networks began outperforming other established models on a number of important benchmarks. Deep neural networks are now the state-of-the-art machine learning models across a variety of areas, from image analysis to natural language processing, and widely deployed in academia and industry. These developments have a huge potential for medical imaging technology, medical data analysis, medical diagnostics and healthcare in general, slowly being realized. We provide a short overview of recent advances and some associated challenges in machine learning applied to medical image processing and image analysis. As this has become a very broad and fast expanding field we will not survey the entire landscape of applications, but put particular focus on deep learning in MRI. Our aim is threefold: (i) give a brief introduction to deep learning with pointers to core references; (ii) indicate how deep learning has been applied to the entire MRI processing chain, from acquisition to image retrieval, from segmentation to disease prediction; (iii) provide a starting point for people interested in experimenting and perhaps contributing to the field of machine learning for medical imaging by pointing out good educational resources, state-of-the-art open-source code, and interesting sources of data and problems related medical imaging.

991 citations