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Wei Dai

Bio: Wei Dai is an academic researcher from Shanghai University. The author has contributed to research in topics: Non-local means & Speckle pattern. The author has an hindex of 2, co-authored 3 publications receiving 140 citations.

Papers
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Journal ArticleDOI
TL;DR: A deep learning architecture for automated extraction of learned-from-data image features from the shear-wave elastography (SWE) that integrates feature learning with feature selection on SWE is built and may be potentially used in clinical computer-aided diagnosis of breast cancer.

172 citations

Journal ArticleDOI
TL;DR: Computer-extracted CEUS features show reduced and more heterogeneous neovascularization of cancer after NAC, and achieve high accuracy for discriminating between pre- and post-chemotherapy cancers in responders and thus are potentially valuable for tumor response evaluation in clinical practice.

7 citations

Proceedings ArticleDOI
01 Oct 2014
TL;DR: A new method for speckle suppression by integrating the non-local means (NLM) with the McIlhagga-based anisotropic diffusion (MAD) to demonstrate its superiority to seven state-of-the-art methods in terms of noise reduction and detail preservation.
Abstract: Speckle noise contaminates medical ultrasound images and the suppression of speckle noise is valuable for image interpretation. This paper presents a new method for speckle suppression by integrating the non-local means (NLM) with the McIlhagga-based anisotropic diffusion (MAD). The MAD is first used to get a diffused image from the initial noisy image, and then the NLM is conducted to get a final denoised image by processing both the initial image and the diffused image. Experimental results on both simulated and real ultrasound images validate the feasibility of the method and demonstrate its superiority to seven state-of-the-art methods in terms of noise reduction and detail preservation.

2 citations


Cited by
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Journal ArticleDOI
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.

8,730 citations

Journal ArticleDOI
TL;DR: This Review addresses the critical issues to ensure the proper development of radiomics as a biomarker and facilitate its implementation in clinical practice.

460 citations

Journal ArticleDOI
TL;DR: Several popular deep learning architectures are briefly introduced, and their applications in various specific tasks in US image analysis, such as classification, detection, and segmentation are discussed.

448 citations

Journal ArticleDOI
17 Aug 2018
TL;DR: It can be noted that the application of deep learning technology is widespread, but the majority of applications are focused on bioinformatics, medical diagnosis and other similar fields.
Abstract: In this review the application of deep learning for medical diagnosis is addressed. A thorough analysis of various scientific articles in the domain of deep neural networks application in the medical field has been conducted. More than 300 research articles were obtained, and after several selection steps, 46 articles were presented in more detail. The results indicate that convolutional neural networks (CNN) are the most widely represented when it comes to deep learning and medical image analysis. Furthermore, based on the findings of this article, it can be noted that the application of deep learning technology is widespread, but the majority of applications are focused on bioinformatics, medical diagnosis and other similar fields.

278 citations

Journal ArticleDOI
TL;DR: A general analysis of the current status of CAD systems according to the used image modalities and the machine learning based classifiers is provided.

245 citations