That younger individuals perceive the world as moving slower than adults is a familiar phenomenon. Yet, it remains an open question why that is. Using event segmentation theory, electroencephalogram (EEG) beamforming and nonlinear causal relationship estimation using artificial neural network methods, we...
Artificial Neural Networks (ANNs) have been proposed as computational models of the primate ventral stream, because their performance on tasks such as image classification rivals or exceeds human baselines. But useful models should not only predict data well, but also offer insights into the systems...
(SVM) and artificial neural network (ANN). For each algorithm we automatically extract diagnosis rules. For formalising expert knowledge, we relied on the normative dataset (Invernizzi et al. in Ophthalmol Retina 2(8):808–815, 2018). For arguing between agents, we used the Jason multi-...
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This MATLAB function returns the gradient-weighted class activation mapping (Grad-CAM) map of the change in the classification score of input X, when the network net evaluates the class score for the class given by classIdx.
Deep learning is a subset of machine learning in artificial intelligence that has network capable of learning unsupervised from data that is unstructured or unlabeled also known as deep neural learning or deep neural network. Different types of deep learning models ...
In recent years, Artificial Intelligence (AI) has proven its relevance for medical decision support. However, the “black-box” nature of successful AI algorithms still holds back their wide-spread deployment. In this paper, we describe an eXplanatory Artificial Intelligence (XAI) that reaches the...
Artificial intelligence (AI) can be trained to recognize whether a tissue image contains a tumor. However, exactly how it makes its decision has remained a mystery until now. A team from the Research Center for Protein Diagnostics (PRODI) at Ruhr-Universität Bochum is developing a new approac...
OMENN: One Matrix to Explain Neural Networks Deep Learning (DL) models are often black boxes, making their decision-making processes difficult to interpret. This lack of transparency has driven advancements in eXplainable Artificial Intelligence (XAI), a field dedicated to clarifying the reasoning ...
This lack of transparency has driven advancements in eXplainable Artificial Intelligence (XAI), a field dedicated to clarifying the reasoning behind DL model predictions. Among these, attribution-based methods such as LRP and GradCAM are widely used, though they rely on approximations that can be ...