Doanh C. Bui

Seoul, Republic of Korea

My name is Doanh C. Bui, and I am currently pursuing a master’s degree in Electrical Engineering at Korea University, where I am in my final semester under the supervision of Prof. Jin Tae Kwak at the QuIIL Laboratory. Prior to this, I obtained a Bachelor’s degree in Computer Science from the University of Information Technology (UIT), VNU-HCM in 2022, where I was supervised by Dr. Khang Nguyen at the UIT-Together Laboratory. From November 2022 to February 2023, I also served as a teaching assistant at the Faculty of Software Engineering at UIT, where I was responsible for lecturing practice credits for the Introduction to Programming course during the Fall semester of 2022.

Throughout my academic journey, I have focused on various aspects of Computer Vision, specifically in areas such as Object Detection, Document Image Understanding, Image Captioning, and Human-Object Interaction. Presently, my research revolves around leveraging image processing techniques for histopathology images.

news

Dec 10, 2024	One paper has been accepted for presentation at AAAI2025 (A*-ranked)
Nov 29, 2024	Defense master thesis about Computational Methods for Multi-Class Cancer Classification
Oct 7, 2024	Placed 6th at MICCAI2024-LEOPARD challenge
Sep 20, 2024	One paper has been accepted for presentation at ACCV2024 (B-ranked)
Aug 6, 2024	One paper has been accepted by Machine Vision and Applications (SCIE Q2, IF = 2.4)
Jul 25, 2024	One paper has been accepted by IEEE Transactions on Medical Imaging (SCIE Q1, IF = 8.9)
Jun 17, 2024	One paper has been accepted for presentation at MICCAI2024 (A-ranked)
Mar 6, 2024	One paper has been accepted by IEEE Transactions on Circuits and Systems for Video Technology (SCIE Q1, IF = 8.4)
Mar 2, 2024	One paper has been accepted by Computer Methods and Programs in Biomedicine (SCIE Q1, IF = 6.1)
Feb 5, 2024	One paper has been accepted for presentation at MICCAI2023-Thompson (1st Place in Track 2 - Visual Question Answering)

selected publications

HiGDA: Hierarchical Graph of Nodes to Learn Local-to-Global Topology for Semi-Supervised Domain Adaptation

Ba Hung Ngo*, Doanh C. Bui*, Nhat-Tuong Do-Tran, and Tae Jong Choi

In 39th Annual AAAI Conference on Artificial Intelligence (AAAI) (Accepted), 2024

Bib PDF

@inproceedings{higda2024,
  author = {Ngo*, Ba Hung and Bui*, Doanh C. and Do-Tran, Nhat-Tuong and Choi, Tae Jong},
  booktitle = {39th Annual AAAI Conference on Artificial Intelligence (AAAI) (Accepted)},
  title = {HiGDA: Hierarchical Graph of Nodes to Learn Local-to-Global Topology for Semi-Supervised Domain Adaptation},
  volume = {},
  pages = {},
  year = {2024},
}

MECFormer: Multi-task Whole Slide Image Classification with Expert Consultation Network

Doanh C. Bui, and Jin Tae Kwak

In 17th Asian Conference on Computer Vision (ACCV) (Accepted), 2024

Bib PDF

@inproceedings{bui2024mecformer,
  author = {Bui, Doanh C. and Kwak, Jin Tae},
  booktitle = {17th Asian Conference on Computer Vision (ACCV) (Accepted)},
  title = {MECFormer: Multi-task Whole Slide Image Classification with Expert Consultation Network},
  volume = {},
  pages = {},
  year = {2024},
}

Transformer with Multi-level Grid Features and Depth Pooling for Image Captioning

Doanh C. Bui, Tam V. Nguyen, and Khang Nguyen

Machine Vision and Applications, Aug 2024

Bib PDF

@article{BuiTMDNet2024,
  author = {Bui, Doanh C. and Nguyen, Tam V. and Nguyen, Khang},
  journal = {Machine Vision and Applications},
  title = {Transformer with Multi-level Grid Features and Depth Pooling for Image Captioning},
  year = {2024},
  month = aug,
  day = {20},
  volume = {35},
  number = {5},
  pages = {118},
}

Spatially-constrained and -unconstrained bi-graph interaction network for multi-organ pathology image classification

Doanh C. Bui, Boram Song, Kyungeun Kim, and Jin Tae Kwak

IEEE Transactions on Medical Imaging, Aug 2024

Bib PDF

@article{scubanet2024,
  author = {Bui, Doanh C. and Song, Boram and Kim, Kyungeun and Kwak, Jin Tae},
  journal = {IEEE Transactions on Medical Imaging},
  title = {Spatially-constrained and -unconstrained bi-graph interaction network for multi-organ pathology image classification},
  year = {2024},
  volume = {},
  number = {},
  pages = {1-1},
  keywords = {Pathology;Transformers;Cancer;Image edge detection;Vectors;Computational modeling;Image classification;Computational pathology;cancer classification;graph neural network;Transformer;interaction},
  doi = {10.1109/TMI.2024.3436080},
}

QuIIL at T3 Challenge: Towards Automation in Life-Saving Intervention Procedures from First-Person View

Trinh T. L. Vuong, Doanh C. Bui, and Jin Tae Kwak

In AI for Brain Lesion Detection and Trauma Video Action Recognition, Aug 2024

Abs Bib PDF

In this paper, we present our solutions for a spectrum of automation tasks in life-saving intervention procedures within the Trauma THOMPSON (T3) Challenge, encompassing action recognition, action anticipation, and Visual Question Answering (VQA). For action recognition and anticipation, we propose a pre-processing strategy that samples and stitches multiple inputs into a single image and then incorporates momentum- and attention-based knowledge distillation to improve the performance of the two tasks. For training, we present an action dictionary-guided design, which consistently yields the most favorable results across our experiments. In the realm of VQA, we leverage object-level features and deploy co-attention networks to train both object and question features. Notably, we introduce a novel frame-question cross-attention mechanism at the network’s core for enhanced performance. Our solutions achieve the }}2^{nd}}}rank in action recognition and anticipation tasks and }}1^{st}}}rank in the VQA task. The source code is available at https://github.com/QuIIL/QuIIL_thompson_solution.
@inproceedings{10.1007/978-3-031-71626-3_10, author = {T. L. Vuong, Trinh and C. Bui, Doanh and Kwak, Jin Tae}, editor = {Bao, Rina and Grant, Ellen and Kirkpatrick, Andrew and Wachs, Juan and Ou, Yangming}, title = {QuIIL at T3 Challenge: Towards Automation in Life-Saving Intervention Procedures from First-Person View}, booktitle = {AI for Brain Lesion Detection and Trauma Video Action Recognition}, year = {2024}, publisher = {Springer Nature Switzerland}, address = {Cham}, pages = {82--93}, isbn = {978-3-031-71626-3}, }

DAX-Net: A dual-branch dual-task adaptive cross-weight feature fusion network for robust multi-class cancer classification in pathology images

Doanh C. Bui, Boram Song, Kyungeun Kim, and Jin Tae Kwak

Computer Methods and Programs in Biomedicine, Aug 2024

Bib PDF

@article{BUI2024108112,
  title = {DAX-Net: A dual-branch dual-task adaptive cross-weight feature fusion network for robust multi-class cancer classification in pathology images},
  journal = {Computer Methods and Programs in Biomedicine},
  volume = {248},
  pages = {108112},
  year = {2024},
  issn = {0169-2607},
  doi = {https://doi.org/10.1016/j.cmpb.2024.108112},
  url = {https://www.sciencedirect.com/science/article/pii/S0169260724001081},
  author = {Bui, Doanh C. and Song, Boram and Kim, Kyungeun and Kwak, Jin Tae},
  keywords = {Cancer classification, Hybrid model, CNN, Transformer, Feature fusion, Multi-task learning},
}

Transformer-based Spatio-Temporal Unsupervised Traffic Anomaly Detection in Aerial Videos

Tung Minh Tran, Doanh C. Bui, Tam V. Nguyen, and Khang Nguyen

IEEE Transactions on Circuits and Systems for Video Technology, Aug 2024

Bib PDF

@article{10466765,
  author = {Tran, Tung Minh and Bui, Doanh C. and Nguyen, Tam V. and Nguyen, Khang},
  journal = {IEEE Transactions on Circuits and Systems for Video Technology},
  title = {Transformer-based Spatio-Temporal Unsupervised Traffic Anomaly Detection in Aerial Videos},
  year = {2024},
  volume = {},
  number = {},
  pages = {1-1},
  keywords = {Surveillance;Anomaly detection;Transformers;Traffic control;Computational modeling;Training;Task analysis;Anomaly Detection;Convolutional Neural Networks;Vision Transformers;Traffic Surveillance;Aerial Images},
  doi = {10.1109/TCSVT.2024.3376399},
}

C2T-Net: Channel-Aware Cross-Fused Transformer-Style Networks for Pedestrian Attribute Recognition

Doanh C. Bui, Thinh V. Le, and Ba Hung Ngo

In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) Workshops, Jan 2024

Bib PDF

@inproceedings{Bui_2024_WACV,
  author = {Bui, Doanh C. and Le, Thinh V. and Ngo, Ba Hung},
  title = {C2T-Net: Channel-Aware Cross-Fused Transformer-Style Networks for Pedestrian Attribute Recognition},
  booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) Workshops},
  month = jan,
  year = {2024},
  pages = {351-358},
}

Improving human–object interaction with auxiliary semantic information and enhanced instance representation

Khang Nguyen, Thinh V Le, Huyen Ngoc N Van, and Doanh C Bui

Pattern Recognition Letters, Jan 2023

Bib PDF

@article{nguyen2023improving,
  title = {Improving human--object interaction with auxiliary semantic information and enhanced instance representation},
  author = {Nguyen, Khang and Le, Thinh V and Van, Huyen Ngoc N and Bui, Doanh C},
  journal = {Pattern Recognition Letters},
  volume = {175},
  pages = {38--43},
  year = {2023},
  publisher = {Elsevier},
  doi = {https://doi.org/10.1016/j.patrec.2023.09.013},
}