6 citations · 10 across the 13 of their papers we have counts for
14 papers
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,…
Who Gets Missed in the Tail? Thresholded Subgroup Underdiagnosis in Long-Tailed Chest X-ray Classification
Ha-Hieu Pham, Hai-Dang Nguyen, Dang P. M. Cao +5
In chest X-ray (CXR) classification, acceptable ranking performance can still leave rare-positive patients below threshold, especially within subgroups. We study this pre-deploymen…
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…