Z
Zongwei Zhou
Researcher at Arizona State University
Publications - 33
Citations - 7177
Zongwei Zhou is an academic researcher from Arizona State University. The author has contributed to research in topics: Transfer of learning & Deep learning. The author has an hindex of 12, co-authored 31 publications receiving 2588 citations. Previous affiliations of Zongwei Zhou include Arizona's Public Universities.
Papers
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Journal ArticleDOI
Transferable Visual Words: Exploiting the Semantics of Anatomical Patterns for Self-Supervised Learning
Fatemeh Haghighi,Mohammad Reza Hosseinzadeh Taher,Zongwei Zhou,Michael B. Gotway,Jianming Liang +4 more
TL;DR: TransVW as discussed by the authors proposes a transferable visual words (TransVW) approach to achieve annotation efficiency for deep learning in medical image analysis, which exploits the semantics of visual words for self-supervised learning, requiring no expert annotation.
Journal ArticleDOI
Active, continual fine tuning of convolutional neural networks for reducing annotation efforts
TL;DR: This paper presents a novel method to naturally integrate active learning and transfer learning (fine-tuning) into a single framework, which starts directly with a pre-trained CNN to seek "worthy" samples for annotation and gradually enhances the CNN via continual fine- Tuning.
Book ChapterDOI
Learning Semantics-Enriched Representation via Self-discovery, Self-classification, and Self-restoration
Fatemeh Haghighi,Mohammad Reza Hosseinzadeh Taher,Zongwei Zhou,Michael B. Gotway,Jianming Liang +4 more
TL;DR: This work trains deep models to learn semantically enriched visual representation by self-discovery, self-classification, and self-restoration of the anatomy underneath medical images, resulting in a semantics-enriched, general-purpose, pre-trained 3D model, named Semantic Genesis.
Journal ArticleDOI
Integrating Active Learning and Transfer Learning for Carotid Intima-Media Thickness Video Interpretation
TL;DR: A new concept, called Annotation Unit (AU), which simplifies the entire CIMT video annotation process down to six simple mouse clicks, and a new algorithm, called AFT (active fine-tuning), which naturally integrates active learning and transfer learning into a single framework.
Posted Content
Learning Semantics-enriched Representation via Self-discovery, Self-classification, and Self-restoration
Fatemeh Haghighi,Mohammad Reza Hosseinzadeh Taher,Zongwei Zhou,Michael B. Gotway,Jianming Liang +4 more
TL;DR: Semantic Genesis as discussed by the authors is a general-purpose, pre-trained 3D model for self-discovery, self-classification, and self-restoration of the anatomy underneath medical images.