activity
20172020
most citedAutomatic Detection of Knee Joints and Quantification of Knee Osteoarthritis Severity using Convolutional Neural Networks

1 citations · 1 across the 2 of their papers we have counts for

collaborators

5 papers

cs.CV2020

Investigating Class-level Difficulty Factors in Multi-label Classification Problems

Mark Marsden, Kevin McGuinness, Joseph Antony +6

This work investigates the use of class-level difficulty factors in multi-label classification problems for the first time. Four class-level difficulty factors are proposed: freque…

eess.IV2019

Predicting knee osteoarthritis severity: comparative modeling based on patient's data and plain X-ray images

Jaynal Abedin, Joseph Antony, Kevin McGuinness +4

Knee osteoarthritis (KOA) is a disease that impairs knee function and causes pain. A radiologist reviews knee X-ray images and grades the severity level of the impairments accordin…

eess.IV2019

Assessing Knee OA Severity with CNN attention-based end-to-end architectures

Marc Górriz, Joseph Antony, Kevin McGuinness +2

This work proposes a novel end-to-end convolutional neural network (CNN) architecture to automatically quantify the severity of knee osteoarthritis (OA) using X-Ray images, which i…

cs.CV2019

Feature Learning to Automatically Assess Radiographic Knee Osteoarthritis Severity

Joseph Antony, Kevin McGuinness, Kieran Moran +1

This chapter presents the investigations and the results of feature learning using convolutional neural networks to automatically assess knee osteoarthritis (OA) severity and the a…

cs.CV20171 cited

Automatic Detection of Knee Joints and Quantification of Knee Osteoarthritis Severity using Convolutional Neural Networks

Joseph Antony, Kevin McGuinness, Kieran Moran +1

This paper introduces a new approach to automatically quantify the severity of knee OA using X-ray images. Automatically quantifying knee OA severity involves two steps: first, aut…