18 citations · 49 across the 13 of their papers we have counts for
7 papers · 1 filter
Flow Matching in Latent Space
Quan Dao, Hao Phung, Binh Nguyen +1
Flow matching is a recent framework to train generative models that exhibits impressive empirical performance while being relatively easier to train compared with diffusion-based m…
LVM-Med: Learning Large-Scale Self-Supervised Vision Models for Medical Imaging via Second-order Graph Matching
Duy M. H. Nguyen, Hoang Nguyen, Nghiem T. Diep +9
Obtaining large pre-trained models that can be fine-tuned to new tasks with limited annotated samples has remained an open challenge for medical imaging data. While pre-trained dee…
Application of Self-Supervised Learning to MICA Model for Reconstructing Imperfect 3D Facial Structures
Phuong D. Nguyen, Thinh D. Le, Duong Q. Nguyen +2
In this study, we emphasize the integration of a pre-trained MICA model with an imperfect face dataset, employing a self-supervised learning approach. We present an innovative meth…
DRG-Net: Interactive Joint Learning of Multi-lesion Segmentation and Classification for Diabetic Retinopathy Grading
Hasan Md Tusfiqur, Duy M. H. Nguyen, Mai T. N. Truong +8
Diabetic Retinopathy (DR) is a leading cause of vision loss in the world, and early DR detection is necessary to prevent vision loss and support an appropriate treatment. In this w…
Joint Self-Supervised Image-Volume Representation Learning with Intra-Inter Contrastive Clustering
Duy M. H. Nguyen, Hoang Nguyen, Mai T. N. Truong +7
Collecting large-scale medical datasets with fully annotated samples for training of deep networks is prohibitively expensive, especially for 3D volume data. Recent breakthroughs i…
Deep Metric Learning Meets Deep Clustering: An Novel Unsupervised Approach for Feature Embedding
Binh X. Nguyen, Binh D. Nguyen, Gustavo Carneiro +3
Unsupervised Deep Distance Metric Learning (UDML) aims to learn sample similarities in the embedding space from an unlabeled dataset. Traditional UDML methods usually use the tripl…