eXplainable Artificial Intelligence (XAI) for the identification of biologically relevant gene expression patterns in longitudinal human studies, insights from obesity research
10.1371/journal.pcbi.1007792 · doi-reference
10.1016/j.csbj.2022.05.056
10.1016/j.csbj.2022.05.056 · doi-reference
10.1109/sibgrapi55357.2022.9991797
10.1109/sibgrapi55357.2022.9991797 · doi-reference
SurvSHAP (t): time-dependent explanations of machine learning survival models
10.1016/j.knosys.2022.110234 · doi-reference
10.1175/1520-0493(1950)0782.0.co;2
10.1175/1520-0493(1950)078<0001:vofeit>2.0.co;2 · doi-reference
10.1016/j.artmed.2023.102697
10.1016/j.artmed.2023.102697 · doi-reference
Explainable artificial intelligence for breast cancer: a visual case-based reasoning approach
10.1016/j.artmed.2019.01.001 · doi-reference
10.1038/s41467-022-34275-9
10.1038/s41467-022-34275-9 · doi-reference
10.1109/tbme.2023.3282840
10.1109/tbme.2023.3282840 · doi-reference
10.1007/978-981-99-3311-2_7
10.1007/978-981-99-3311-2_7 · doi-reference
10.1016/j.csbj.2021.01.010
10.1016/j.csbj.2021.01.010 · doi-reference
XOmiVAE: an interpretable deep learning model for cancer classification using high-dimensional omics data
10.1093/bib/bbab315 · doi-reference
10.1109/jbhi.2023.3316750
10.1109/jbhi.2023.3316750 · doi-reference
10.1016/j.jbi.2023.104365
10.1016/j.jbi.2023.104365 · doi-reference
On generating trustworthy counterfactual explanations
10.1016/j.ins.2023.119898 · doi-reference
Never tell me the odds: Investigating pro-hoc explanations in medical decision making
10.1016/j.artmed.2024.102819 · doi-reference
Dermatologist-like explainable AI enhances trust and confidence in diagnosing melanoma
10.1038/s41467-023-43095-4 · doi-reference
10.1038/s42256-019-0048-x
10.1038/s42256-019-0048-x · doi-reference
10.1101/2024.03.15.24304211
10.1101/2024.03.15.24304211 · doi-reference
10.1145/3584371.3613013
10.1145/3584371.3613013 · doi-reference
10.1038/s41580-021-00407-0
10.1038/s41580-021-00407-0 · doi-reference
10.1038/s41576-021-00434-9
10.1038/s41576-021-00434-9 · doi-reference
Omics data preprocessing for machine learning: a case study in childhood obesity
10.3390/genes14020248 · doi-reference
10.1109/cvpr.2009.5206848
10.1109/cvpr.2009.5206848 · doi-reference
10.1109/cvpr.2018.00474
10.1109/cvpr.2018.00474 · doi-reference
A transfer learning and explainable solution to detect mpox from smartphones images
10.1016/j.pmcj.2023.101874 · doi-reference
Multi-orientation local texture features for guided attention-based fusion in lung nodule classification
10.1109/access.2023.3243104 · doi-reference
10.1016/j.media.2023.102809
10.1016/j.media.2023.102809 · doi-reference
10.1007/978-3-031-16443-9_67
10.1007/978-3-031-16443-9_67 · doi-reference
10.1109/smartworld-uic-atc-scalcom-digitaltwin-pricomp-metaverse56740.2022.00068
10.1109/smartworld-uic-atc-scalcom-digitaltwin-pricomp-metaverse56740.2022.00068 · doi-reference
10.1109/cvprw.2017.114
10.1109/cvprw.2017.114 · doi-reference
Ensembling EfficientNets for the classification and interpretation of histopathology images
10.3390/a14100278 · doi-reference
10.3390/bioengineering9090475
10.3390/bioengineering9090475 · doi-reference
Addressing uncertainty in imbalanced histopathology image classification of HER2 breast cancer: an interpretable ensemble approach with threshold filtered single instance evaluation (SIE)
10.1109/access.2023.3327898 · doi-reference
10.1038/s41591-022-02134-1
10.1038/s41591-022-02134-1 · doi-reference
10.1109/icnsc55942.2022.10004157
10.1109/icnsc55942.2022.10004157 · doi-reference
Non-small cell lung cancer diagnosis aid with histopathological images using explainable deep learning techniques
10.1016/j.cmpb.2022.107108 · doi-reference
Texture analysis for muscular dystrophy classification in MRI with improved class activation mapping
10.1016/j.patcog.2018.08.012 · doi-reference
10.1109/cvpr.2016.319
10.1109/cvpr.2016.319 · doi-reference
10.1109/mlsp.2018.8516998
10.1109/mlsp.2018.8516998 · doi-reference
