J
Jinzheng Cai
Researcher at University of Florida
Publications - 55
Citations - 1687
Jinzheng Cai is an academic researcher from University of Florida. The author has contributed to research in topics: Segmentation & Deep learning. The author has an hindex of 17, co-authored 55 publications receiving 1065 citations. Previous affiliations of Jinzheng Cai include National Institutes of Health.
Papers
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Journal ArticleDOI
Pathologist-level interpretable whole-slide cancer diagnosis with deep learning
Zizhao Zhang,Pingjun Chen,Mason McGough,Fuyong Xing,Chunbao Wang,Marilyn M. Bui,Yuanpu Xie,Manish Sapkota,Lei Cui,Jasreman Dhillon,Nazeel Ahmad,Farah Khalil,Shohreh I. Dickinson,Xiaoshuang Shi,Fujun Liu,Hai Su,Jinzheng Cai,Lin Yang +17 more
TL;DR: A novel pathology whole-slide diagnosis method, powered by artificial intelligence, to address the lack of interpretable diagnosis, which provides an innovative and reliable means for making diagnostic suggestions and can be deployed at low cost as next-generation, artificial intelligence-enhanced CAD technology for use in diagnostic pathology.
Journal ArticleDOI
Uncertainty-aware multi-view co-training for semi-supervised medical image segmentation and domain adaptation
Yingda Xia,Dong Yang,Zhiding Yu,Fengze Liu,Jinzheng Cai,Lequan Yu,Zhuotun Zhu,Daguang Xu,Alan L. Yuille,Holger R. Roth +9 more
TL;DR: This paper proposes uncertainty-aware multi-view co-training (UMCT), a unified framework that addresses these two tasks for volumetric medical image segmentation and can even effectively handle the challenging situation where labeled source data is inaccessible.
Posted Content
Improving Deep Pancreas Segmentation in CT and MRI Images via Recurrent Neural Contextual Learning and Direct Loss Function
TL;DR: This work proposes a new convolutional/recurrent neural network architecture to address the contextual learning and segmentation consistency problem and outperforms the state-of-the-art work on CT and MRI pancreas segmentation, respectively.
Posted Content
Pancreas Segmentation in MRI using Graph-Based Decision Fusion on Convolutional Neural Networks
TL;DR: This paper formulate pancreas segmentation in magnetic resonance imaging (MRI) scans as a graph based decision fusion process combined with deep convolutional neural networks (CNN) to achieve the best results compared with other state of the arts.
Book ChapterDOI
Pancreas Segmentation in MRI Using Graph-Based Decision Fusion on Convolutional Neural Networks
TL;DR: Wang et al. as mentioned in this paper proposed a new convolutional/recurrent neural network architecture to address the contextual learning and segmentation consistency problem, which can be fine-tuned for contextual learning, in an end-to-end manner.