Nhat-Tan Bui
This is a likely match — the affiliation was inferred from OpenAlex, ORCID, and web sources but has not been fully confirmed. Treat with appropriate caution.
Researcher
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)
Faculty Researcher
Research Areas
Links
Biography and Research Information
OverviewAI-generated summary
Nhat-Tan Bui's research focuses on the application of deep learning techniques for medical image analysis, particularly in the segmentation of polyps and the analysis of volumetric medical data. His recent work includes the development of novel network architectures such as MEGANet and PEFNet, which employ multi-scale edge-guided attention and positional embedding features to improve polyp segmentation accuracy. Bui has also contributed to the adaptation of large foundation models, like the Segment Anything Model (SAM), for use in volumetric medical imaging (SAM3D). His research extends to real-time anomaly detection in electrocardiogram (ECG) data using multimodal time and spectrogram restoration networks (TSRNET). Bui collaborates with researchers at the University of Arkansas at Fayetteville, including Ngan Le and Trong Thang Pham, with whom he has co-authored multiple publications. His work has resulted in a citation count of 290 across 22 publications, with an h-index of 7.
Metrics
- h-index: 7
- Publications: 22
- Citations: 328
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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