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Dat T. Ngo
Researcher at Ho Chi Minh City University of Technology
Publications - 26
Citations - 312
Dat T. Ngo is an academic researcher from Ho Chi Minh City University of Technology. The author has contributed to research in topics: Computer science & Engineering. The author has an hindex of 6, co-authored 13 publications receiving 127 citations.
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Journal ArticleDOI
Interpreting chest X-rays via CNNs that exploit hierarchical disease dependencies and uncertainty labels
TL;DR: In this article, a supervised multi-label classification framework based on deep convolutional neural networks (CNNs) was proposed for predicting the presence of 14 common thoracic diseases and observations.
Interpreting Chest X-rays via CNNs that Exploit Hierarchical Disease Dependencies and Uncertainty Labels
TL;DR: In this article, a multi-label classification framework based on deep convolutional neural networks (CNNs) was proposed for diagnosing the presence of 14 common thoracic diseases and observations.
Posted Content
VinDr-CXR: An open dataset of chest X-rays with radiologist's annotations
Ha Q. Nguyen,László György,Khanh Lam,Linh Le,Hieu H. Pham,Dat Q. Tran,Dung B. Nguyen,Dung D. Le,Chi M. Pham,Hang T. T. Tong,Diep H. Dinh,Cuong D. Do,Luu T. Doan,Cuong Ngoc Nguyen,Binh Thanh Nguyen,Que V. Nguyen,Au D. Hoang,Hien N. Phan,Anh Nguyen,Phuong H. Ho,Dat T. Ngo,Nghia T. Nguyen,Nhan Trung Nguyen,Minh Dao,Van Tan Vu +24 more
TL;DR: In this paper, the authors describe a dataset of more than 100,000 chest X-ray scans that were retrospectively collected from two major hospitals in Vietnam and release 18,000 images that were manually annotated by a total of 17 experienced radiologists with 22 local labels of rectangles surrounding abnormalities and 6 global labels of suspected diseases.
Posted Content
Interpreting chest X-rays via CNNs that exploit disease dependencies and uncertainty labels
TL;DR: In this paper, a supervised multi-label classification framework based on deep convolutional neural networks (CNNs) was proposed for predicting the risk of 14 common thoracic diseases.
Proceedings ArticleDOI
Deep Q-Learning with Multiband Sensing for Dynamic Spectrum Access
TL;DR: This work proposes to use the deep Q-learning method to learn a state-action value function that determines an access policy from the observed states of all channels, and demonstrates through experiments that the learning-based policies consistently achieve performances that are close to the optimal ones.