Ordinal Classification
17 papers with code • 1 benchmarks • 0 datasets
Benchmarks
These leaderboards are used to track progress in Ordinal Classification
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Most implemented papers
Improving the repeatability of deep learning models with Monte Carlo dropout
andreanne-lemay/gray_zone_assessment
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During model development and evaluation, much attention is given to classification performance while model repeatability is rarely assessed, leading to the development of models that are unusable in clinical practice.
Controlling Class Layout for Deep Ordinal Classification via Constrained Proxies Learning
tenvence/cpl
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For deep ordinal classification, learning a well-structured feature space specific to ordinal classification is helpful to properly capture the ordinal nature among classes.
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.
A generalized framework to predict continuous scores from medical ordinal labels
andreanne-lemay/gray_zone_assessment
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These labels are used to train and evaluate disease severity prediction models.
Ordinal Classification with Distance Regularization for Robust Brain Age Prediction
jaygshah/Robust-Brain-Age-Prediction
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IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) 2023
However, these methods are subject to an inherent regression to the mean effect, which causes a systematic bias resulting in an overestimation of brain age in young subjects and underestimation in old subjects.
Ordinal classification for interval-valued data and interval-valued functional data
The aim of ordinal classification is to predict the ordered labels of the output from a set of observed inputs.
Conformal Risk Control for Ordinal Classification
As a natural extension to the standard conformal prediction method, several conformal risk control methods have been recently developed and applied to various learning problems.
