activity
20172023
most citedClinically-Inspired Multi-Agent Transformers for Disease Trajectory Forecasting from Multimodal Data

40 citations · 116 across the 19 of their papers we have counts for

collaborators

20 papers

eess.IV2023

End-To-End Prediction of Knee Osteoarthritis Progression With Multi-Modal Transformers

Egor Panfilov, Simo Saarakkala, Miika T. Nieminen +1

Knee Osteoarthritis (KOA) is a highly prevalent chronic musculoskeletal condition with no currently available treatment. The manifestation of KOA is heterogeneous and prediction of…

eess.IV2023

Deep Learning for Predicting Progression of Patellofemoral Osteoarthritis Based on Lateral Knee Radiographs, Demographic Data and Symptomatic Assessments

Neslihan Bayramoglu, Martin Englund, Ida K. Haugen +2

In this study, we propose a novel framework that utilizes deep learning (DL) and attention mechanisms to predict the radiographic progression of patellofemoral osteoarthritis (PFOA…

eess.IV2022★ 3 cited

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…

eess.IV2022★ 40 cited

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…

eess.IV2022★ 18 cited

Predicting Knee Osteoarthritis Progression from Structural MRI using Deep Learning

Egor Panfilov, Simo Saarakkala, Miika T. Nieminen +1

Accurate prediction of knee osteoarthritis (KOA) progression from structural MRI has a potential to enhance disease understanding and support clinical trials. Prior art focused on…

eess.IV2021

Machine Learning Based Texture Analysis of Patella from X-Rays for Detecting Patellofemoral Osteoarthritis

Neslihan Bayramoglu, Miika T. Nieminen, Simo Saarakkala

Objective is to assess the ability of texture features for detecting radiographic patellofemoral osteoarthritis (PFOA) from knee lateral view radiographs. We used lateral view knee…