40 citations · 46 across the 12 of their papers we have counts for
12 papers
Landmark-free Assessment of Lower-limb Alignment with Implicit Neural Shape Functions from Knee Radiographs
Zhisen Hu, Antti Kemppainen, David Johnson +5
Radiographic assessment of lower-limb alignment (LLA) is important for predicting joint health and surgical outcomes in total knee arthroplasty. Traditional measurement methods are…
Bayesian Optimization over Bounded Domains with the Beta Product Kernel
Huy Hoang Nguyen, Han Zhou, Matthew B. Blaschko +1
Bayesian optimization with Gaussian processes (GP) is commonly used to optimize black-box functions. The Matérn and the Radial Basis Function (RBF) covariance functions are used fr…
LoG-VMamba: Local-Global Vision Mamba for Medical Image Segmentation
Trung Dinh Quoc Dang, Huy Hoang Nguyen, Aleksei Tiulpin
Mamba, a State Space Model (SSM), has recently shown competitive performance to Convolutional Neural Networks (CNNs) and Transformers in Natural Language Processing and general seq…
Toward Cost-efficient Adaptive Clinical Trials in Knee Osteoarthritis with Reinforcement Learning
Khanh Nguyen, Huy Hoang Nguyen, Egor Panfilov +1
Osteoarthritis (OA) is the most common musculoskeletal disease, with knee OA (KOA) being one of the leading causes of disability and a significant economic burden. Predicting KOA p…
Image-level Regression for Uncertainty-aware Retinal Image Segmentation
Trung Dang, Huy Hoang Nguyen, Aleksei Tiulpin
Accurate retinal vessel (RV) segmentation is a crucial step in the quantitative assessment of retinal vasculature, which is needed for the early detection of retinal diseases and o…
SiNGR: Brain Tumor Segmentation via Signed Normalized Geodesic Transform Regression
Trung Dang, Huy Hoang Nguyen, Aleksei Tiulpin
One of the primary challenges in brain tumor segmentation arises from the uncertainty of voxels close to tumor boundaries. However, the conventional process of generating ground tr…
Universal Graph Continual Learning
Thanh Duc Hoang, Do Viet Tung, Duy-Hung Nguyen +3
We address catastrophic forgetting issues in graph learning as incoming data transits from one to another graph distribution. Whereas prior studies primarily tackle one setting of…
A Stronger Baseline For Automatic Pfirrmann Grading Of Lumbar Spine MRI Using Deep Learning
Narasimharao Kowlagi, Huy Hoang Nguyen, Terence McSweeney +4
This paper addresses the challenge of grading visual features in lumbar spine MRI using Deep Learning. Such a method is essential for the automatic quantification of structural cha…
Clinically-Inspired Multi-Agent Transformers for Disease Trajectory Forecasting from Multimodal Data
Huy Hoang Nguyen, Matthew B. Blaschko, Simo Saarakkala +1
Deep neural networks are often applied to medical images to automate the problem of medical diagnosis. However, a more clinically relevant question that practitioners usually face…
AdaTriplet: Adaptive Gradient Triplet Loss with Automatic Margin Learning for Forensic Medical Image Matching
Khanh Nguyen, Huy Hoang Nguyen, Aleksei Tiulpin
This paper tackles the challenge of forensic medical image matching (FMIM) using deep neural networks (DNNs). FMIM is a particular case of content-based image retrieval (CBIR). The…
CLIMAT: Clinically-Inspired Multi-Agent Transformers for Knee Osteoarthritis Trajectory Forecasting
Huy Hoang Nguyen, Simo Saarakkala, Matthew B. Blaschko +1
In medical applications, deep learning methods are built to automate diagnostic tasks. However, a clinically relevant question that practitioners usually face, is how to predict th…
Semixup: In- and Out-of-Manifold Regularization for Deep Semi-Supervised Knee Osteoarthritis Severity Grading from Plain Radiographs
Huy Hoang Nguyen, Simo Saarakkala, Matthew Blaschko +1
Knee osteoarthritis (OA) is one of the highest disability factors in the world. This musculoskeletal disorder is assessed from clinical symptoms, and typically confirmed via radiog…