6 citations · 10 across the 13 of their papers we have counts for
12 papers · 1 filter
Asymmetric Paired-Annotation Learning for Multi-Structure ULF Pediatric Brain MRI Segmentation
Ha-Hieu Pham, Dang P. M. Cao, Minh Hoang Pham +4
Portable ultra-low-field (ULF) MRI can expand access to pediatric neuroimaging, but segmentation at 0.064 T remains challenging because anatomical boundaries are weakly delineated,…
ESC: Emotional Self-Correction for Reliable Vision-Language Models
Tien-Huy Nguyen, Minh-Nhat Nguyen, Nguyen Nhat Huy +9
Vision-language models (VLMs) have achieved strong performance across diverse multimodal tasks, yet they remain vulnerable to unreliable reasoning. Existing self-correction methods…
From Specialist to Generalist: Unlocking SAM's Learning Potential on Unlabeled Medical Images
Vi Vu, Thanh-Huy Nguyen, Tien-Thinh Nguyen +5
Foundation models like the Segment Anything Model (SAM) show strong generalization, yet adapting them to medical images remains difficult due to domain shift, scarce labels, and th…
Domain-invariant Mixed-domain Semi-supervised Medical Image Segmentation with Clustered Maximum Mean Discrepancy Alignment
Ba-Thinh Lam, Thanh-Huy Nguyen, Hoang-Thien Nguyen +5
Deep learning has shown remarkable progress in medical image semantic segmentation, yet its success heavily depends on large-scale expert annotations and consistent data distributi…
Scribble-Supervised Medical Image Segmentation with Dynamic Teacher Switching and Hierarchical Consistency
Thanh-Huy Nguyen, Hoang-Loc Cao, Dat T. Chung +5
Scribble-supervised methods have emerged to mitigate the prohibitive annotation burden in medical image segmentation. However, the inherent sparsity of these annotations introduces…
Modality-Specific Enhancement and Complementary Fusion for Semi-Supervised Multi-Modal Brain Tumor Segmentation
Tien-Dat Chung, Ba-Thinh Lam, Thanh-Huy Nguyen +5
Semi-supervised learning (SSL) has become a promising direction for medical image segmentation, enabling models to learn from limited labeled data alongside abundant unlabeled samp…
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…
Adaptive Knowledge Transferring with Switching Dual-Student Framework for Semi-Supervised Medical Image Segmentation
Hoang-Thien Nguyen, Thanh-Huy Nguyen, Ba-Thinh Lam +6
Teacher-student frameworks have emerged as a leading approach in semi-supervised medical image segmentation, demonstrating strong performance across various tasks. However, the lea…
Semi-MoE: Mixture-of-Experts meets Semi-Supervised Histopathology Segmentation
Nguyen Lan Vi Vu, Thanh-Huy Nguyen, Thien Nguyen +4
Semi-supervised learning has been employed to alleviate the need for extensive labeled data for histopathology image segmentation, but existing methods struggle with noisy pseudo-l…
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…