Tran Minh Quan
Duyệt theo
After receiving a Master’s degree in Electrical Engineering from KAIST, Dr. Tran Minh Quan continued to complete his PhD program in Computer Science at Ulsan National Institute of Science and Technology (Korea). Dr. Quan has published 14 research works in prestigious international scientific journals. His research focuses on the application of computer science in biomedical image processing, improving the quality of magnetic resonance imaging (MRI).
Các tài liệu mới cập nhật
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FusionNet: A Deep Fully Residual Convolutional Neural Network for Image Segmentation in Connectomics
(2021-05-13)Cellular-resolution connectomics is an ambitious research direction with the goal of generating comprehensive brain connectivity maps using high-throughput, nano-scale electron microscopy. One of the main challenges in ... -
Silicon surface lattice resonances and halide perovskite semiconductors for exciton-polaritons at room temperature
(2023-01-01)Owing to their high oscillator strength, binding energy, and low-cost fabrication, two-dimensional halide perovskites have recently gained attention as excellent materials for generating exciton-polaritons at room temperature. ... -
XPGAN: X-ray projected generative adversarial network for improving COVID-19 image classification
(2021-04-13)This work aims to fight against the current outbreak pandemic by developing a method to classify suspected infected COVID-19 cases. Driven by the urgency, due to the vastly increased number of patients and deaths worldwide, ... -
ZeVis: A Visual Analytics System for Exploration of a Larval Zebrafish Brain in Serial-Section Electron Microscopy Images
(2021-05-26)The automation and improvement of nano-scale electron microscopy imaging technologies have expanded a push in neuroscience to understand brain circuits at the scale of individual cells and their connections. Most of this ... -
GOAL: Gist-set online active learning for efficient chest X-ray image annotation
(2021)Deep learning in medical image analysis often requires extensive high-quality labeled data to achieve human-level accuracy. We propose Gist-set Online Active Learning (GOAL), a novel solution to the challenge of limited ... -
FusionNet: A Deep Fully Residual Convolutional Neural Network for Image Segmentation in Connectomics
(2021-05-13)Cellular-resolution connectomics is an ambitious research direction with the goal of generating comprehensive brain connectivity maps using high-throughput, nano-scale electron microscopy. One of the main challenges in ... -
Neural Radiance Projection
(2022-03-20)The proposed method, Neural Radiance Projection (NeRP), addresses three fundamental challenges in training convolutional neural networks for X-ray image segmentation: handling limited or missing human-annotated datasets, ... -
ColorRL: Reinforced Coloring for End-to-End Instance Segmentation
(2021)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. Although many feed-forward networks produce ...
