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Found 13 papers, 4 papers with code
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.
Unlearning Spurious Correlations in Chest X-ray Classification
We train a deep learning model using a Covid-19 chest X-ray dataset and we showcase how this dataset can lead to spurious correlations due to unintended confounding regions.
Learning from Exemplary Explanations
eXplanation Based Learning (XBL) is a form of Interactive Machine Learning (IML) that provides a model refining approach via user feedback collected on model 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.
Identifying Spurious Correlations and Correcting them with an Explanation-based Learning
Identifying spurious correlations learned by a trained model is at the core of refining a trained model and building a trustworthy model.
Impact of Feedback Type on Explanatory Interactive Learning
Explanatory Interactive Learning (XIL) collects user feedback on visual model explanations to implement a Human-in-the-Loop (HITL) based interactive learning scenario.
Posture Prediction for Healthy Sitting using a Smart Chair
Wearable sensors, pressure or force sensors, videos and images were used for posture recognition in the literature.
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).
Automated Smartphone based System for Diagnosis of Diabetic Retinopathy
Automated diagnoses of diabetic retinopathy can be deployed on smartphones in order to provide an instant diagnosis to diabetic people residing in remote areas.
Diagnosis of Diabetic Retinopathy in Ethiopia: Before the Deep Learning based Automation
Introducing automated Diabetic Retinopathy (DR) diagnosis into Ethiopia is still a challenging task, despite recent reports that present trained Deep Learning (DL) based DR classifiers surpassing manual graders.
Point-of-Care Diabetic Retinopathy Diagnosis: A Standalone Mobile Application Approach
Methods to exploit deep learning applications in healthcare have been proposed and implemented in this dissertation.
Transfer Learning based Detection of Diabetic Retinopathy from Small Dataset
Annotated training data insufficiency remains to be one of the challenges of applying deep learning in medical data classification problems.
