6 citations · 8 across the 10 of their papers we have counts for
15 papers
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…
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…
DiLO: Disentangled Latent Optimization for Learning Shape and Deformation in Grouped Deforming 3D Objects
Mostofa Rafid Uddin, Jana Armouti, Umong Sain +3
In this work, we propose a disentangled latent optimization-based method for parameterizing grouped deforming 3D objects into shape and deformation factors in an unsupervised manne…
Towards Foundation Models for Cryo-ET Subtomogram Analysis
Runmin Jiang, Wanyue Feng, Yuntian Yang +11
Cryo-electron tomography (cryo-ET) enables in situ visualization of macromolecular structures, where subtomogram analysis tasks such as classification, alignment, and averaging are…
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…
TextCAM: Explaining Class Activation Map with Text
Qiming Zhao, Xingjian Li, Xiaoyu Cao +2
Deep neural networks (DNNs) have achieved remarkable success across domains but remain difficult to interpret, limiting their trustworthiness in high-stakes applications. This pape…