H
Hyunseok Seo
Researcher at Stanford University
Publications - 36
Citations - 636
Hyunseok Seo is an academic researcher from Stanford University. The author has contributed to research in topics: Segmentation & Deep learning. The author has an hindex of 8, co-authored 32 publications receiving 331 citations. Previous affiliations of Hyunseok Seo include KAIST & Korea Institute of Science and Technology.
Papers
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Journal ArticleDOI
Modified U-Net (mU-Net) With Incorporation of Object-Dependent High Level Features for Improved Liver and Liver-Tumor Segmentation in CT Images
TL;DR: In this paper, a residual path with deconvolution and activation operations was added to the skip connection of the U-Net to avoid duplication of low-resolution information of features.
Journal ArticleDOI
Machine Learning Techniques for Biomedical Image Segmentation: An Overview of Technical Aspects and Introduction to State-of-Art Applications
Hyunseok Seo,Masoud Badiei Khuzani,Varun Vasudevan,Charles Huang,Hongyi Ren,Ruoxiu Xiao,Xiao Jia,Lei Xing +7 more
TL;DR: This review article highlights the imperative role of machine learning algorithms in enabling efficient and accurate segmentation in the field of medical imaging and discusses several challenges related to the training of different machine learning models, and presents some heuristics to address those challenges.
Patent
Method and apparatus for providing information in mobile terminal
TL;DR: In this paper, an information provision method and apparatus of a mobile terminal is provided for managing and providing information items associated with a plurality of communication accounts of a user in an integrated manner.
Journal ArticleDOI
Automatic Polyp Recognition in Colonoscopy Images Using Deep Learning and Two-Stage Pyramidal Feature Prediction
TL;DR: The proposed PLPNet method can effectively detect polyps in colonoscopy images and generate high-quality segmentation masks in a pixel-to-pixel manner and corroborates that CNNs with very deep architecture and richer semantics are highly efficient in medical image learning and inference.
Journal ArticleDOI
Machine learning techniques for biomedical image segmentation: An overview of technical aspects and introduction to state-of-art applications
Hyunseok Seo,Masoud Badiei Khuzani,Varun Vasudevan,Charles Huang,Hongyi Ren,Ruoxiu Xiao,Xiao Jia,Lei Xing +7 more
TL;DR: In this article, the authors highlight the imperative role of machine learning algorithms in enabling efficient and accurate segmentation in the field of medical imaging and highlight the challenges related to the training of different machine learning models, and present some heuristics to address those challenges.