Now showing items 1-8 of 8

    • ColorRL: Reinforced Coloring for End-to-End Instance Segmentation 

      Tran, Minh Quan; Tran, Anh Tuan; Nguyen, Tuan Khoa; Jeong, Won-Ki (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 ...
    • GOAL: Gist-set online active learning for efficient chest X-ray image annotation 

      Nguyen, T. D. Chanh; Huynh, Minh Thanh; Tran, Minh Quan; Nguyen, Ngoc Hoang; Jain, Mudit; Ngo, Van Doan; Vo, Tan Duc; Bui, H. Trung; Truong, Steven QH (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 ...
    • XPGAN: X-ray projected generative adversarial network for improving COVID-19 image classification 

      Quan, Tran Minh; Thanh, Huynh Minh; Huy, Ta Duc; Chanh, Nguyen Do Trung; Anh, Nguyen Thi Phuong; Vu, Phan Hoan; Nam, Nguyen Hoang; Tuong, Tran Quy; Dien, Vu Minh; Giang, Bui Van; Trung, Bui Huu; Truong, Steven Quoc Hung (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, ...
    • FusionNet: A Deep Fully Residual Convolutional Neural Network for Image Segmentation in Connectomics 

      Tran, Minh Quan; Hildebrand, David Grant Colburn; Jeong, Won-Ki (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 ...
    • FusionNet: A Deep Fully Residual Convolutional Neural Network for Image Segmentation in Connectomics 

      Quan, Tran Minh; Hildebrand, David Grant Colburn; Jeong, Won-Ki (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 ...
    • ZeVis: A Visual Analytics System for Exploration of a Larval Zebrafish Brain in Serial-Section Electron Microscopy Images 

      Choi, Junyoung; Hildebrand, David Grant Colburn; Moon, Jungmin; Quan, Tran Minh; Tuan, Tran Anh; Ko, Sungahn; Jeong, Won-Ki (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 ...
    • Neural Radiance Projection 

      Pham, Ngoc Huy; Tran, Minh Quan (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, ...
    • Silicon surface lattice resonances and halide perovskite semiconductors for exciton-polaritons at room temperature 

      Nguyen, Dinh Hai; Nguyen, Sy Khiem; Tran, Minh Quan; Le, Viet Hoang; Trinh, Quoc Trung; Bui, Son Tung; Bui, Xuan Khuyen; Vu, Dinh Lam; Nguyen, Hai-Son; Le-Van, Quynh (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. ...

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