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Dinggang Shen

Researcher at ShanghaiTech University

Publications -  1380
Citations -  66294

Dinggang Shen is an academic researcher from ShanghaiTech University. The author has contributed to research in topics: Segmentation & Image registration. The author has an hindex of 109, co-authored 1350 publications receiving 50446 citations. Previous affiliations of Dinggang Shen include University of Pennsylvania & Veterans Health Administration.

Papers
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Journal ArticleDOI

Deep Learning in Medical Image Analysis

Dinggang Shen, +2 more
- 20 Jun 2017 - 
TL;DR: This review covers computer-assisted analysis of images in the field of medical imaging and introduces the fundamentals of deep learning methods and their successes in image registration, detection of anatomical and cellular structures, tissue segmentation, computer-aided disease diagnosis and prognosis, and so on.
Posted ContentDOI

Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

Spyridon Bakas, +438 more
- 05 Nov 2018 - 
TL;DR: This study assesses the state-of-the-art machine learning methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018, and investigates the challenge of identifying the best ML algorithms for each of these tasks.
Journal ArticleDOI

HAMMER: hierarchical attribute matching mechanism for elastic registration

Dinggang Shen, +1 more
- 01 Nov 2002 - 
TL;DR: A new approach is presented for elastic registration of medical images, and is applied to magnetic resonance images of the brain, where it results in accurate superposition of image data from individuals with significant anatomical differences.
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Multimodal Classification of Alzheimer’s Disease and Mild Cognitive Impairment

Daoqiang Zhang, +4 more
- 01 Apr 2011 - 
TL;DR: Three modalities of biomarkers are proposed to combine, i.e., MRI, FDG-PET, and CSF biomarkers, to discriminate between AD (or MCI) and healthy controls, using a kernel combination method, and shows considerably better performance, compared to the case of using an individual modality of biomarker.
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Review of Artificial Intelligence Techniques in Imaging Data Acquisition, Segmentation, and Diagnosis for COVID-19

Feng Shi, +8 more
- 01 Jan 2021 - 
TL;DR: This review paper covers the entire pipeline of medical imaging and analysis techniques involved with COVID-19, including image acquisition, segmentation, diagnosis, and follow-up, and particularly focuses on the integration of AI with X-ray and CT, both of which are widely used in the frontline hospitals.