Search Results for author:
Found 8 papers, 5 papers with code
An AI System for Continuous Knee Osteoarthritis Severity Grading Using Self-Supervised Anomaly Detection with Limited Data
This work proposes a three stage approach for automated continuous grading of knee OA that is built upon the principles of Anomaly Detection (AD); learning a robust representation of healthy knee X-rays and grading disease severity based on its distance to the centre of normality.
Rethinking Knee Osteoarthritis Severity Grading: A Few Shot Self-Supervised Contrastive Learning Approach
Currently, radiologists grade the severity of OA on an ordinal scale from zero to four using the Kellgren-Lawrence (KL) system.
Distance-Aware eXplanation Based Learning
eXplanation Based Learning (XBL) is an interactive learning approach that provides a transparent method of training deep learning models by interacting with their explanations.
Interpretable Weighted Siamese Network to Predict the Time to Onset of Alzheimer's Disease from MRI Images
To this end, we select progressive MCI patients from the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset and construct an ordinal dataset with a prediction target that indicates the time to progression to AD.
FewSOME: One-Class Few Shot Anomaly Detection with Siamese Networks
Our experiments demonstrate that FewSOME performs at state-of-the-art level on benchmark datasets MNIST, CIFAR-10, F-MNIST and MVTec AD while training on only 30 normal samples, a minute fraction of the data that existing methods are trained on.
Optimising Knee Injury Detection with Spatial Attention and Validating Localisation Ability
As MRI data is acquired from three planes, we compare our technique using data from a single-plane and multiple planes (multi-plane).
Semi-Supervised Siamese Network for Identifying Bad Data in Medical Imaging Datasets
Noisy data present in medical imaging datasets can often aid the development of robust models that are equipped to handle real-world data.
A Simplistic Machine Learning Approach to Contact Tracing
This report is based on the modified NIST challenge, Too Close For Too Long, provided by the SFI Centre for Machine Learning (ML-Labs).
