no code implementations • 19 Apr 2023 • Pham Ngoc Huy, Tran Minh Quan
In this study, we introduce a generative model that can synthesize a large number of radiographical image/label pairs, and thus is asymptotically favorable to downstream activities such as segmentation in bio-medical image analysis.
no code implementations • 15 Mar 2022 • Pham Ngoc Huy, Tran Minh Quan
The proposed method, Neural Radiance Projection (NeRP), addresses the three most fundamental shortages of training such a convolutional neural network on X-ray image segmentation: dealing with missing/limited human-annotated datasets; ambiguity on the per-pixel label; and the imbalance across positive- and negative- classes distribution.
no code implementations • CVPR 2021 • Tran Anh Tuan, Nguyen Tuan Khoa, Tran Minh Quan, Won-Ki Jeong
Instance segmentation, the task of identifying and separating each individual object of interest in the image, is one of the actively studied research topics in computer vision.
no code implementations • 14 May 2020 • Tuan Tran Anh, Khoa Nguyen-Tuan, Tran Minh Quan, Won-Ki Jeong
To exploit the advantages of conventional single-object-per-step segmentation methods without impairing the scalability, we propose a novel iterative deep reinforcement learning agent that learns how to differentiate multiple objects in parallel.
1 code implementation • 3 Sep 2017 • Tran Minh Quan, Thanh Nguyen-Duc, Won-Ki Jeong
In this paper, we propose a novel deep learning-based generative adversarial model, RefineGAN, for fast and accurate CS-MRI reconstruction.
6 code implementations • 16 Dec 2016 • Tran Minh Quan, David G. C. Hildebrand, Won-Ki Jeong
Electron microscopic connectomics is an ambitious research direction with the goal of studying comprehensive brain connectivity maps by using high-throughput, nano-scale microscopy.