6 citations · 17 across the 10 of their papers we have counts for
11 papers
Importance-Aware OBS Pruning for Diffusion Models
Ba-Thinh Lam, Srijan Das, Hieu Le
We propose importance-aware pruning for diffusion models, a training-free framework that prioritizes preserving parameters critical to semantically salient image regions. To do so,…
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…
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…
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…
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…
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…
Describe Anything in Medical Images
Xi Xiao, Yunbei Zhang, Thanh-Huy Nguyen +10
Localized image captioning has made significant progress with models like the Describe Anything Model (DAM), which can generate detailed region-specific descriptions without explic…
Towards Robust Natural-Looking Mammography Lesion Synthesis on Ipsilateral Dual-Views Breast Cancer Analysis
Thanh-Huy Nguyen, Quang Hien Kha, Thai Ngoc Toan Truong +4
In recent years, many mammographic image analysis methods have been introduced for improving cancer classification tasks. Two major issues of mammogram classification tasks are lev…
Delving into Ipsilateral Mammogram Assessment under Multi-View Network
Thai Ngoc Toan Truong, Thanh-Huy Nguyen, Ba Thinh Lam +2
In many recent years, multi-view mammogram analysis has been focused widely on AI-based cancer assessment. In this work, we aim to explore diverse fusion strategies (average and co…