7 citations · 43 across the 22 of their papers we have counts for
7 papers · 1 filter
DuetMatch: Harmonizing Semi-Supervised Brain MRI Segmentation via Decoupled Branch Optimization
Thanh-Huy Nguyen, Hoang-Thien Nguyen, Vi Vu +6
The limited availability of annotated data in medical imaging makes semi-supervised learning increasingly appealing for its ability to learn from imperfect supervision. Recently, t…
UP2D: Uncertainty-aware Progressive Pseudo-label Denoising for Source-Free Domain Adaptive Medical Image Segmentation
Quang-Khai Bui-Tran, Thanh-Huy Nguyen, Manh D. Ho +5
Medical image segmentation models face severe performance drops under domain shifts, especially when data sharing constraints prevent access to source images. We present a novel Un…
Aligning What You Separate: Denoised Patch Mixing for Source-Free Domain Adaptation in Medical Image Segmentation
Quang-Khai Bui-Tran, Thanh-Huy Nguyen, Hoang-Thien Nguyen +5
Source-Free Domain Adaptation (SFDA) is emerging as a compelling solution for medical image segmentation under privacy constraints, yet current approaches often ignore sample diffi…
Label-Efficient Cross-Modality Generalization for Liver Segmentation in Multi-Phase MRI
Quang-Khai Bui-Tran, Minh-Toan Dinh, Thanh-Huy Nguyen +3
Accurate liver segmentation in multi-phase MRI is vital for liver fibrosis assessment, yet labeled data is often scarce and unevenly distributed across imaging modalities and vendo…
Learning Disentangled Stain and Structural Representations for Semi-Supervised Histopathology Segmentation
Ha-Hieu Pham, Nguyen Lan Vi Vu, Thanh-Huy Nguyen +4
Accurate gland segmentation in histopathology images is essential for cancer diagnosis and prognosis. However, significant variability in Hematoxylin and Eosin (H&E) staining and t…
Describe Anything Model for Visual Question Answering on Text-rich Images
Yen-Linh Vu, Dinh-Thang Duong, Truong-Binh Duong +8
Recent progress has been made in region-aware vision-language modeling, particularly with the emergence of the Describe Anything Model (DAM). DAM is capable of generating detailed…
GMAT: Grounded Multi-Agent Clinical Description Generation for Text Encoder in Vision-Language MIL for Whole Slide Image Classification
Ngoc Bui Lam Quang, Nam Le Nguyen Binh, Thanh-Huy Nguyen +3
Multiple Instance Learning (MIL) is the leading approach for whole slide image (WSI) classification, enabling efficient analysis of gigapixel pathology slides. Recent work has intr…