Nhat-Tan Bui
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Also affiliated: Vietnam National University Ho Chi Minh City (2022–2023); Ho Chi Minh City University of Science (2022–2023); Auckland University of Technology (2022–2023)
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Biography and Research Information
OverviewAI-generated summary
Nhat-Tan Bui's research focuses on the application of machine learning and deep learning techniques, particularly in the area of medical image analysis. His recent publications highlight work on polyp segmentation in medical images, including the development of networks like MEGANet, SAM3D, and PEFNet. He has also investigated real-time anomaly detection in ECG data using a framework called TSRNET, and explored 3D object retrieval for SHREC'22.
Bui collaborates with several researchers at the University of Arkansas at Fayetteville, including Ngan Le, Trong Thang Pham, Susan Gauch, and Ngoc-Vuong Ho, with whom he has co-authored multiple publications. His scholarly output includes 22 publications with a total of 334 citations and an h-index of 7.
Metrics
- h-index: 7
- Publications: 22
- Citations: 334
Selected Publications
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Synthetic Dataset for Understanding Negation in Text-Guided Image Editing (2025)Journal of the Arkansas Academy of Science OpenAlex
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NeIn: Telling What You Don't Want (2025)
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FG-CXR: A Radiologist-Aligned Gaze Dataset for Enhancing Interpretability in Chest X-Ray Report Generation (2024)
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PGDS: Pose-Guidance Deep Supervision for Mitigating Clothes-Changing in Person Re-Identification (2024)
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MEGANet: Multi-Scale Edge-Guided Attention Network for Weak Boundary Polyp Segmentation (2024)
Collaboration Network
Top Collaborators
- MEGANet: Multi-Scale Edge-Guided Attention Network for Weak Boundary Polyp Segmentation
- PGDS: Pose-Guidance Deep Supervision for Mitigating Clothes-Changing in Person Re-Identification
- NeIn: Telling What You Don't Want
- MEGANet: Multi-Scale Edge-Guided Attention Network for Weak Boundary Polyp Segmentation
- PGDS: Pose-Guidance Deep Supervision for Mitigating Clothes-Changing in Person Re-Identification
- NeIn: Telling What You Don't Want
- MEGANet: Multi-Scale Edge-Guided Attention Network for Weak Boundary Polyp Segmentation
- PGDS: Pose-Guidance Deep Supervision for Mitigating Clothes-Changing in Person Re-Identification
- PGDS: Pose-Guidance Deep Supervision for Mitigating Clothes-Changing in Person Re-Identification
- NeIn: Telling What You Don't Want
- MEGANet: Multi-Scale Edge-Guided Attention Network for Weak Boundary Polyp Segmentation
- PGDS: Pose-Guidance Deep Supervision for Mitigating Clothes-Changing in Person Re-Identification
- PGDS: Pose-Guidance Deep Supervision for Mitigating Clothes-Changing in Person Re-Identification
- PGDS: Pose-Guidance Deep Supervision for Mitigating Clothes-Changing in Person Re-Identification
- PGDS: Pose-Guidance Deep Supervision for Mitigating Clothes-Changing in Person Re-Identification
- FG-CXR: A Radiologist-Aligned Gaze Dataset for Enhancing Interpretability in Chest X-Ray Report Generation
- FG-CXR: A Radiologist-Aligned Gaze Dataset for Enhancing Interpretability in Chest X-Ray Report Generation
- FG-CXR: A Radiologist-Aligned Gaze Dataset for Enhancing Interpretability in Chest X-Ray Report Generation
- FG-CXR: A Radiologist-Aligned Gaze Dataset for Enhancing Interpretability in Chest X-Ray Report Generation
- FG-CXR: A Radiologist-Aligned Gaze Dataset for Enhancing Interpretability in Chest X-Ray Report Generation
- FG-CXR: A Radiologist-Aligned Gaze Dataset for Enhancing Interpretability in Chest X-Ray Report Generation
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