Interpretability of clinical decision support systems based on artificial intelligence from technological and medical perspective: A systematic review
Q Xu, W Xie, B Liao, C Hu, L Qin, Z Yang… - Journal of healthcare …, 2023 - Wiley Online Library
Background. Artificial intelligence (AI) has developed rapidly, and its application extends to
clinical decision support system (CDSS) for improving healthcare quality. However, the …
clinical decision support system (CDSS) for improving healthcare quality. However, the …
This looks like those: Illuminating prototypical concepts using multiple visualizations
We present ProtoConcepts, a method for interpretable image classification combining deep
learning and case-based reasoning using prototypical parts. Existing work in prototype …
learning and case-based reasoning using prototypical parts. Existing work in prototype …
A Systematic Approach for Explaining Time and Frequency Features Extracted by Convolutional Neural Networks From Raw Electroencephalography Data
In recent years, the use of convolutional neural networks (CNNs) for raw resting-state
electroencephalography (EEG) analysis has grown increasingly common. However, relative …
electroencephalography (EEG) analysis has grown increasingly common. However, relative …
Manipulating feature visualizations with gradient slingshots
Deep Neural Networks (DNNs) are capable of learning complex and versatile
representations, however, the semantic nature of the learned concepts remains unknown. A …
representations, however, the semantic nature of the learned concepts remains unknown. A …
FPN-IAIA-BL: A Multi-Scale Interpretable Deep Learning Model for Classification of Mass Margins in Digital Mammography
Digital mammography is essential to breast cancer detection and deep learning offers
promising tools for faster and more accurate mammogram analysis. In radiology and other …
promising tools for faster and more accurate mammogram analysis. In radiology and other …
A Framework for Systematically Evaluating the Representations Learned by A Deep Learning Classifier from Raw Multi-Channel Electroencephalogram Data
The application of deep learning methods to raw electroencephalogram (EEG) data is
growing increasingly common. While these methods offer the possibility of improved …
growing increasingly common. While these methods offer the possibility of improved …
A novel activation maximization-based approach for insight into electrophysiology classifiers
Spectral analysis remains a hallmark approach for gaining insight into electrophysiology
modalities like electroencephalography (EEG). As the field of deep learning has progressed …
modalities like electroencephalography (EEG). As the field of deep learning has progressed …
CoSy: Evaluating Textual Explanations of Neurons
A crucial aspect of understanding the complex nature of Deep Neural Networks (DNNs) is
the ability to explain learned concepts within their latent representations. While various …
the ability to explain learned concepts within their latent representations. While various …
A systematic approach for explaining time and frequency features extracted by CNNs from raw EEG data
In recent years, the use of convolutional neural networks (CNNs) for raw
electroencephalography (EEG) analysis has grown increasingly common. However, relative …
electroencephalography (EEG) analysis has grown increasingly common. However, relative …
[HTML][HTML] Identifying EEG Biomarkers of Depression with Novel Explainable Deep Learning Architectures
Deep learning methods are increasingly being applied to raw electroencephalogram (EEG)
data. However, if these models are to be used in clinical or research contexts, methods to …
data. However, if these models are to be used in clinical or research contexts, methods to …