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20172024
most citedDLIME: A Deterministic Local Interpretable Model-Agnostic Explanations Approach for Computer-Aided Diagnosis Systems

57 citations · 89 across the 20 of their papers we have counts for

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5 papers · 1 filter

eess.IV20242 cited

A Dynamically Weighted Loss Function for Unsupervised Image Segmentation

Boujemaa Guermazi, Riadh Ksantini, Naimul Khan

Image segmentation is the foundation of several computer vision tasks, where pixel-wise knowledge is a prerequisite for achieving the desired target. Deep learning has shown promis…

eess.IV20231 cited

Structure Preserving Cycle-GAN for Unsupervised Medical Image Domain Adaptation

Paolo Iacono, Naimul Khan

The presence of domain shift in medical imaging is a common issue, which can greatly impact the performance of segmentation models when dealing with unseen image domains. Adversari…

eess.IV2021

Online unsupervised Learning for domain shift in COVID-19 CT scan datasets

Nicolas Ewen, Naimul Khan

Neural networks often require large amounts of expert annotated data to train. When changes are made in the process of medical imaging, trained networks may not perform as well, an…

eess.IV2020

Targeted Self Supervision for Classification on a Small COVID-19 CT Scan Dataset

Nicolas Ewen, Naimul Khan

Traditionally, convolutional neural networks need large amounts of data labelled by humans to train. Self supervision has been proposed as a method of dealing with small amounts of…

eess.IV2020

Interpreting Uncertainty in Model Predictions For COVID-19 Diagnosis

Gayathiri Murugamoorthy, Naimul Khan

COVID-19, due to its accelerated spread has brought in the need to use assistive tools for faster diagnosis in addition to typical lab swab testing. Chest X-Rays for COVID cases te…