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Found 3 papers, 1 papers with code
To Trust or Not to Trust: Towards a novel approach to measure trust for XAI systems
The increasing reliance on Deep Learning models, combined with their inherent lack of transparency, has spurred the development of a novel field of study known as eXplainable AI (XAI) methods.
An Encoder-Decoder CNN for Hair Removal in Dermoscopic Images
In this work, we present a model based on convolutional neural networks for hair removal in dermoscopic images.
Sickle-cell disease diagnosis support selecting the most appropriate machinelearning method: Towards a general and interpretable approach for cellmorphology analysis from microscopy images
We also defined how to select the most important features for classification to decrease the complexity and the training time, and for interpretability purpose in opaque models.
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