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» Authors » Hoo-Chang Shin

Hoo-Chang Shin

Explore the profile of Hoo-Chang Shin including associated specialties, affiliations and a list of published articles. Areas
Snapshot
Articles 5
Citations 1974
Followers 0
Related Specialties
Biology
Biomedical Engineering
Medical Informatics
Top 10 Co-Authors
Ronald M Summers
Ziyue Xu
Le Lu
Mingchen Gao
Daniel J Mollura
Jason J Corso
Holger R Roth
Lukrecia Mertova
D Louis Collins
Raj Jena
Published In
IEEE Trans Med Imaging
IEEE Trans Pattern Anal Mach Intell
Database (Oxford)
Comput Methods Biomech Biomed Eng Imaging Vis
Affiliations
Soon will be listed here.
Recent Articles
1.
Chemical identification and indexing in full-text articles: an overview of the NLM-Chem track at BioCreative VII
Leaman R, Islamaj R, Adams V, Alliheedi M, Almeida J, Antunes R, et al.
Database (Oxford) . 2023 Mar; 2023. PMID: 36882099
The BioCreative National Library of Medicine (NLM)-Chem track calls for a community effort to fine-tune automated recognition of chemical names in the biomedical literature. Chemicals are one of the most...
2.
Holistic classification of CT attenuation patterns for interstitial lung diseases via deep convolutional neural networks
Gao M, Bagci U, Lu L, Wu A, Buty M, Shin H, et al.
Comput Methods Biomech Biomed Eng Imaging Vis . 2018 Apr; 6(1):1-6. PMID: 29623248
Interstitial lung diseases (ILD) involve several abnormal imaging patterns observed in computed tomography (CT) images. Accurate classification of these patterns plays a significant role in precise clinical decision making of...
3.
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
Shin H, Roth H, Gao M, Lu L, Xu Z, Nogues I, et al.
IEEE Trans Med Imaging . 2016 Feb; 35(5):1285-98. PMID: 26886976
Remarkable progress has been made in image recognition, primarily due to the availability of large-scale annotated datasets and deep convolutional neural networks (CNNs). CNNs enable learning data-driven, highly representative, hierarchical...
4.
The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS)
Menze B, Jakab A, Bauer S, Kalpathy-Cramer J, Farahani K, Kirby J, et al.
IEEE Trans Med Imaging . 2014 Dec; 34(10):1993-2024. PMID: 25494501
In this paper we report the set-up and results of the Multimodal Brain Tumor Image Segmentation Benchmark (BRATS) organized in conjunction with the MICCAI 2012 and 2013 conferences. Twenty state-of-the-art...
5.
Stacked autoencoders for unsupervised feature learning and multiple organ detection in a pilot study using 4D patient data
Shin H, Orton M, Collins D, Doran S, Leach M
IEEE Trans Pattern Anal Mach Intell . 2013 Jun; 35(8):1930-43. PMID: 23787345
Medical image analysis remains a challenging application area for artificial intelligence. When applying machine learning, obtaining ground-truth labels for supervised learning is more difficult than in many more common applications...