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Found 13 papers, 5 papers with code
Accelerating Cardiac MRI Reconstruction with CMRatt: An Attention-Driven Approach
This study aims to explore the untapped potential of attention mechanisms incorporated with a deep learning model within the context of the CMR reconstruction problem.
Lightweight Framework for Automated Kidney Stone Detection using coronal CT images
In this paper, we propose a lightweight fusion framework for kidney detection and kidney stone diagnosis on coronal CT images.
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.
Evaluate Fine-tuning Strategies for Fetal Head Ultrasound Image Segmentation with U-Net
This method addresses the challenges associated with training a CNN network from scratch.
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.
Cardiac Segmentation using Transfer Learning under Respiratory Motion Artifacts
Methods that are resilient to artifacts in the cardiac magnetic resonance imaging (MRI) while performing ventricle segmentation, are crucial for ensuring quality in structural and functional analysis of those tissues.
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.
