Explainable artificial intelligence to increase transparency for revolutionizing healthcare ecosystem and the road ahead
The integration of deep learning (DL) into co-clinical applications has generated substantial
interest among researchers aiming to enhance clinical decision support systems for various …
interest among researchers aiming to enhance clinical decision support systems for various …
Uncertainty-aware and lesion-specific image synthesis in multiple sclerosis magnetic resonance imaging: a multicentric validation study
T Finck, H Li, S Schlaeger, L Grundl… - Frontiers in …, 2022 - frontiersin.org
Generative adversarial networks (GANs) can synthesize high-contrast MRI from lower-
contrast input. Targeted translation of parenchymal lesions in multiple sclerosis (MS), as well …
contrast input. Targeted translation of parenchymal lesions in multiple sclerosis (MS), as well …
A dataset generation framework for evaluating megapixel image classifiers and their explanations
Deep learning-based megapixel image classifiers have exceptional prediction performance
in a number of domains, including clinical pathology. However, extracting reliable, human …
in a number of domains, including clinical pathology. However, extracting reliable, human …
On the robustness of explanations of deep neural network models: A survey
A Jyoti, KB Ganesh, M Gayala, NL Tunuguntla… - arXiv preprint arXiv …, 2022 - arxiv.org
Explainability has been widely stated as a cornerstone of the responsible and trustworthy
use of machine learning models. With the ubiquitous use of Deep Neural Network (DNN) …
use of machine learning models. With the ubiquitous use of Deep Neural Network (DNN) …
[图书][B] Computer Vision–ECCV 2022: 17th European Conference, Tel Aviv, Israel, October 23–27, 2022, Proceedings, Part XX
The 39-volume set, comprising the LNCS books 13661 until 13699, constitutes the refereed
proceedings of the 17th European Conference on Computer Vision, ECCV 2022, held in Tel …
proceedings of the 17th European Conference on Computer Vision, ECCV 2022, held in Tel …
Prospector heads: generalized feature attribution for large models & data
Feature attribution, the ability to localize regions of the input data that are relevant for
classification, is an important capability for ML models in scientific and biomedical domains …
classification, is an important capability for ML models in scientific and biomedical domains …
Rethinking Robustness of Model Attributions
S Kamath, S Mittal, A Deshpande… - Proceedings of the …, 2024 - ojs.aaai.org
For machine learning models to be reliable and trustworthy, their decisions must be
interpretable. As these models find increasing use in safety-critical applications, it is …
interpretable. As these models find increasing use in safety-critical applications, it is …
Can we trust explainable ai methods on asr? an evaluation on phoneme recognition
Explainable AI (XAI) techniques have been widely used to help explain and understand the
output of deep learning models in fields such as image classification and Natural Language …
output of deep learning models in fields such as image classification and Natural Language …
Building trust in deep learning-based immune response predictors with interpretable explanations
The ability to predict whether a peptide will get presented on Major Histocompatibility
Complex (MHC) class I molecules has profound implications in designing vaccines …
Complex (MHC) class I molecules has profound implications in designing vaccines …
A Quantitative Approach for Evaluating Disease Focus and Interpretability of Deep Learning Models for Alzheimer's Disease Classification
Deep learning (DL) models have shown significant potential in Alzheimer's Disease (AD)
classification. However, understanding and interpreting these models remains challenging …
classification. However, understanding and interpreting these models remains challenging …