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13 papers · ranked by Valyu relevance

01
BbioRxiv8 Jan 2025
A Single-Graph Visualization to Reveal Hidden Explainability Patterns of SHAP Feature Interactions in Machine Learning for Biomedical Issues
Félix Furger, Julien Aligon, Miguel Thomas, Emmanuel Doumard + 3 more
In the last decades, the utility of Machine Learning (ML) in the biomedical domain has been demonstrated repeatedly. Their inherent opacity need augmenting ML with explainability techniques. A common practice in model explainability however, is to focus solely on the explanatory values themselves without accounting for…
85relevance
02
BbioRxiv8 Oct 2022
Towards Greater Neuroimaging Classification Transparency via the Integration of Explainability Methods and Confidence Estimation Approaches
Charles A. Ellis, Robyn L. Miller, Vince D. Calhoun
The field of neuroimaging has increasingly sought to develop artificial intelligence-based models for neurological and neuropsychiatric disorder automated diagnosis and clinical decision support. However, if these models are to be implemented in a clinical setting, transparency will be vital. Two aspects of…
84relevance
03
BbioRxiv25 Oct 2019
RFEX: Simple Random Forest Model and Sample Explainer for non-Machine Learning experts
D. Petkovic, A. Alavi, D. Cai, J. Yang + 1 more
Machine Learning (ML) is becoming an increasingly critical technology in many areas. However, its complexity and its frequent non-transparency create significant challenges, especially in the biomedical and health areas. One of the critical components in addressing the above challenges is the explainability or…
83relevance
04
BbioRxiv10 Apr 2025
Discernibility in explanations: an approach to designing more acceptable and meaningful machine learning models for medicine
Haomiao Wang, Julien Aligon, Julien May, Emmanuel Doumard + 6 more
Although the benefits of machine learning (ML) are undeniable in health-care, explainability plays a vital role in improving transparency and understanding the most decisive and persuasive variables for prediction. The challenge is to identify explanations that make sense to the biomedical expert. This work proposes…
83relevance
05
BbioRxiv24 Feb 2025
Sensitivity based model agnostic scalable explanations of deep learning
Manu Aggarwal, NG Cogan, Vipul Periwal
Deep neural networks (DNNs) are powerful tools for data-driven predictive machine learning, but their complex architecture obscures mechanistic relations that they have learned from data. This information is critical to the scientific method of hypotheses development, experiment design, and model validation, especially…
83relevance
06
BbioRxiv14 Jul 2021
A Gradient-based Spectral Explainability Method for EEG Deep Learning Classifiers
Charles A. Ellis, Mohammad S.E. Sendi, Robyn L. Miller, Vince D. Calhoun
The automated feature extraction capabilities of deep learning classifiers have promoted their broader application to EEG analysis. In contrast to earlier machine learning studies that used extracted features and traditional explainability approaches, explainability for classifiers trained on raw data is particularly…
79relevance
07
BbioRxiv18 Feb 2025
Ensemble Post-hoc Explainable AI in Multivariate Time Series: Identifying Medical Features Driving Disease Prediction
Jacqueline Michelle Metsch, Philip Hempel, Miriam Cindy Maurer, Nicolai Spicher + 1 more
Despite the growing success of deep learning (DL) in multivariate time-series classification, such as 12-lead electrocardiography (ECG), widespread integration into clinical practice has yet to be achieved. The limited transparency of DL hinders clinical adoption, where understanding model decisions is crucial for…
78relevance
08
BbioRxiv11 Jun 2021
A Novel Local Explainability Approach for Spectral Insight into Raw Eeg-Based Deep Learning Classifiers
Charles A. Ellis, Robyn L. Miller, Vince D. Calhoun
The frequency domain of electroencephalography (EEG) data has developed as a particularly important area of EEG analysis. EEG spectra have been analyzed with explainable machine learning and deep learning methods. However, as deep learning has developed, most studies use raw EEG data, which is not well-suited for…
77relevance
09
BbioRxiv2 Dec 2022
Chimeric U-Net – Modifying the standard U-Net towards Explainability
Kenrick Schulze, Felix Peppert, Christof Schütte, Vikram Sunkara
Healthcare guided by semantic segmentation has the potential to improve our quality of life through early and accurate disease detection. Convolutional Neural Networks, especially the U-Net-based architectures, are currently the state-of-the-art learningbased segmentation methods and have given unprecedented…
77relevance
10
BbioRxiv14 May 2021
An Explainable Deep Learning Approach for Multimodal Electrophysiology Classification
Charles A. Ellis, Darwin A. Carbajal, Rongen Zhang, Robyn L. Miller + 2 more
In recent years, more biomedical studies have begun to use multimodal data to improve model performance. As such, there is a need for improved multimodal explainability methods. Many studies involving multimodal explainability have used ablation approaches. Ablation requires the modification of input data, which may…
77relevance
11
BbioRxiv21 Mar 2023
A Framework for Systematically Evaluating the Representations Learned by A Deep Learning Classifier from Raw Multi-Channel Electroencephalogram Data
Charles A. Ellis, Abhinav Sattiraju, Robyn L. Miller, Vince D. Calhoun
The application of deep learning methods to raw electroencephalogram (EEG) data is growing increasingly common. While these methods offer the possibility of improved performance relative to other approaches applied to manually engineered features, they also present the problem of reduced explainability. As such, a…
75relevance
12
BbioRxiv13 Jan 2022
Explainable t-SNE for single-cell RNA-seq data analysis
Henry Han, Tianyu Zhang, Mary Lauren Benton, Chun Li + 2 more
Single-cell RNA (scRNA-seq) sequencing technologies trigger the study of individual cell gene expression and reveal the diversity within cell populations. To measure cell-to-cell similarity based on their transcription and gene expression, many dimension reduction methods are employed to retrieve the corresponding…
75relevance
13
BbioRxiv10 Feb 2022
A Systematic Approach for Explaining Time and Frequency Features Extracted by CNNs from Raw EEG Data
Charles A. Ellis, Robyn L. Miller, Vince D. Calhoun
In recent years, the use of convolutional neural networks (CNNs) for raw electroencephalography (EEG) analysis has grown increasingly common. However, relative to earlier machine learning and deep learning methods with manually extracted features, CNNs for raw EEG analysis present unique problems for explainability. As…
74relevance

Results for Explainability analysis

A Single-Graph Visualization to Reveal Hidden Explainability Patterns of SHAP Feature Interactions in Machine Learning for Biomedical Issues

Towards Greater Neuroimaging Classification Transparency via the Integration of Explainability Methods and Confidence Estimation Approaches

RFEX: Simple Random Forest Model and Sample Explainer for non-Machine Learning experts

Discernibility in explanations: an approach to designing more acceptable and meaningful machine learning models for medicine

Sensitivity based model agnostic scalable explanations of deep learning

A Gradient-based Spectral Explainability Method for EEG Deep Learning Classifiers

Ensemble Post-hoc Explainable AI in Multivariate Time Series: Identifying Medical Features Driving Disease Prediction

A Novel Local Explainability Approach for Spectral Insight into Raw Eeg-Based Deep Learning Classifiers

Chimeric U-Net – Modifying the standard U-Net towards Explainability

An Explainable Deep Learning Approach for Multimodal Electrophysiology Classification

A Framework for Systematically Evaluating the Representations Learned by A Deep Learning Classifier from Raw Multi-Channel Electroencephalogram Data

Explainable t-SNE for single-cell RNA-seq data analysis

A Systematic Approach for Explaining Time and Frequency Features Extracted by CNNs from Raw EEG Data

A Single-Graph Visualization to Reveal Hidden Explainability Patterns of SHAP Feature Interactions in Machine Learning for Biomedical Issues

Towards Greater Neuroimaging Classification Transparency via the Integration of Explainability Methods and Confidence Estimation Approaches

RFEX: Simple Random Forest Model and Sample Explainer for non-Machine Learning experts

Discernibility in explanations: an approach to designing more acceptable and meaningful machine learning models for medicine

Sensitivity based model agnostic scalable explanations of deep learning

A Gradient-based Spectral Explainability Method for EEG Deep Learning Classifiers

Ensemble Post-hoc Explainable AI in Multivariate Time Series: Identifying Medical Features Driving Disease Prediction

A Novel Local Explainability Approach for Spectral Insight into Raw Eeg-Based Deep Learning Classifiers

Chimeric U-Net – Modifying the standard U-Net towards Explainability

An Explainable Deep Learning Approach for Multimodal Electrophysiology Classification

A Framework for Systematically Evaluating the Representations Learned by A Deep Learning Classifier from Raw Multi-Channel Electroencephalogram Data

Explainable t-SNE for single-cell RNA-seq data analysis

A Systematic Approach for Explaining Time and Frequency Features Extracted by CNNs from Raw EEG Data