Abstract
Dost Muhammad
Abstract
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Brain tumor segmentation of mri images: a comprehensive review on the application of artificial intelligence tools
10.1016/j.compbiomed.2022.106405 · 2023
Applying deep learning in digital breast tomosynthesis for automatic breast cancer detection: a review
10.1016/j.media.2021.102049 · 2021
Recent advances in deep learning applications for retinal diagnosis using oct
2020
Unresolved referenced work
2021
Unresolved referenced work
Kept as external metadata until matched
Unresolved referenced work
Kept as external metadata until matched
Darpa's explainable artificial intelligence (xai) program
2019
10.1609/aimag.v38i3.2741
10.1609/aimag.v38i3.2741
Unbox the black-box for the medical explainable ai via multi-modal and multi-centre data fusion: a mini-review, two showcases and beyond
10.1016/j.inffus.2021.07.016 · 2022
Unresolved referenced work
2023
Explainable ai for healthcare 5.0: opportunities and challenges
10.1109/access.2022.3197671 · 2022
User-centric explainability in healthcare: a knowledge-level perspective of informed machine learning
2022
Unresolved referenced work
2023
Explainable medical imaging ai needs human-centered design: guidelines and evidence from a systematic review
10.1038/s41746-022-00699-2 · 2022
Explainable deep learning methods in medical image classification: a survey
10.1145/3625287 · 2023
A survey on deep learning and explainability for automatic report generation from medical images
10.1145/3522747 · 2022
Explainable convolutional neural networks: a taxonomy, review, and future directions
10.1145/3563691 · 2023
Explainable artificial intelligence methods in combating pandemics: a systematic review
10.1109/rbme.2022.3185953 · 2022
Explainable artificial intelligence (xai) in deep learning-based medical image analysis
10.1016/j.media.2022.102470 · 2022
10.1016/j.compbiomed.2023.106668
10.1016/j.compbiomed.2023.106668
Explainable ai in medical imaging: an overview for clinical practitioners–saliency-based xai approaches
10.1016/j.ejrad.2023.110787 · 2023
Explainable ai in medical imaging: an overview for clinical practitioners–beyond saliency-based xai approaches
10.1016/j.ejrad.2023.110786 · 2023
Unresolved referenced work
2021
Demystifying brain tumor segmentation networks: interpretability and uncertainty analysis
10.3389/fncom.2020.00006 · 2020
Assessment of convolutional neural networks for automated classification of chest radiographs
10.1148/radiol.2018181422 · 2019
10.1038/s42256-019-0138-9
10.1038/s42256-019-0138-9
A model-agnostic, network theory-based framework for supporting xai on classifiers
10.1016/j.eswa.2023.122588 · 2024
Explainable AI for medical data: current methods, limitations, and future directions
2023
10.1016/j.inffus.2023.101805
10.1016/j.inffus.2023.101805
Neural additive models: interpretable machine learning with neural nets
2021
Explainable deep learning models in medical image analysis
10.3390/jimaging6060052 · 2020
Explainable deep learning for efficient and robust pattern recognition: a survey of recent developments
10.1016/j.patcog.2021.108102 · 2021
10.1145/2939672.2939778
10.1145/2939672.2939778 · 2016
Game theory interpretation of digital soil mapping convolutional neural networks
2020
Unresolved referenced work
2020
Unresolved referenced work
2016
10.1016/j.knosys.2019.105237
10.1016/j.knosys.2019.105237
10.1109/iccv.2017.74
10.1109/iccv.2017.74
10.5244/c.34.146
10.5244/c.34.146
10.1109/wacv.2018.00097
10.1109/wacv.2018.00097 · 2018
10.1007/s11263-019-01228-7
10.1007/s11263-019-01228-7 · doi-reference
10.1038/s42256-019-0048-x
10.1038/s42256-019-0048-x · doi-reference
10.1007/978-3-030-33850-3_1
10.1007/978-3-030-33850-3_1 · doi-reference
Explaining the black-box smoothly—a counterfactual approach
10.1016/j.media.2022.102721 · doi-reference
10.1038/s41591-020-01192-7
10.1038/s41591-020-01192-7 · doi-reference
Explainable ai and mass surveillance system-based healthcare framework to combat covid-i9 like pandemics
10.1109/mnet.011.2000458 · doi-reference
Automated prediction of covid-19 severity upon admission by chest x-ray images and clinical metadata aiming at accuracy and explainability
10.1038/s41598-023-30505-2 · doi-reference
xvitcos: explainable vision transformer based covid-19 screening using radiography
10.1109/jtehm.2021.3134096 · doi-reference
10.1145/3616131.3616137
10.1145/3616131.3616137 · doi-reference
Deep learning referral suggestion and tumour discrimination using explainable artificial intelligence applied to multiparametric mri
10.1007/s00330-023-09710-0 · doi-reference
Towards trustworthy ai in dentistry
10.1177/00220345221106086 · doi-reference
10.1109/icip46576.2022.9897529
10.1109/icip46576.2022.9897529 · doi-reference
Fairness-related performance and explainability effects in deep learning models for brain image analysis
10.1117/1.jmi.9.6.061102 · doi-reference
Explainable deep learning-based clinical decision support engine for mri-based automated diagnosis of temporomandibular joint anterior disk displacement
10.1016/j.cmpb.2023.107465 · doi-reference
Clinical interpretable deep learning model for glaucoma diagnosis
10.1109/jbhi.2019.2949075 · doi-reference
Explainable multi-instance and multi-task learning for covid-19 diagnosis and lesion segmentation in ct images
10.1016/j.knosys.2022.109278 · doi-reference
Tuberculosis detection in chest radiograph using convolutional neural network architecture and explainable artificial intelligence
10.1007/s00521-022-07258-6 · doi-reference
Layer-wise relevance propagation for explaining deep neural network decisions in mri-based Alzheimer's disease classification
10.3389/fnagi.2019.00194 · doi-reference
Assessment of knee pain from mr imaging using a convolutional Siamese network
10.1007/s00330-020-06658-3 · doi-reference
Deep-learning-assisted diagnosis for knee magnetic resonance imaging: development and retrospective validation of mrnet
10.1371/journal.pmed.1002699 · doi-reference
Doctor's dilemma: evaluating an explainable subtractive spatial lightweight convolutional neural network for brain tumor diagnosis
10.1145/3457187 · doi-reference
An interpretable classifier for high-resolution breast cancer screening images utilizing weakly supervised localization
10.1016/j.media.2020.101908 · doi-reference
Volumetric breast density estimation on mri using explainable deep learning regression
10.1038/s41598-020-75167-6 · doi-reference
Predicting the evolution of pain relief: ensemble learning by diversifying model explanations
10.1145/3466781 · doi-reference
10.1145/3581641.3584044
10.1145/3581641.3584044 · doi-reference
10.1145/3581641.3584075
10.1145/3581641.3584075 · doi-reference
10.1145/3575879.3576014
10.1145/3575879.3576014 · doi-reference
Prediction of quality of life in people with als: on the road towards explainable clinical decision support
10.1145/3477127.3477128 · doi-reference
10.1145/3472163.3472175
10.1145/3472163.3472175 · doi-reference
Explanation-driven hci model to examine the mini-mental state for Alzheimer's disease
10.1145/3527174 · doi-reference
Improving explainable ai with patch perturbation-based evaluation pipeline: a covid-19 x-ray image analysis case study
10.1038/s41598-023-46493-2 · doi-reference
10.3390/s22249983
10.3390/s22249983 · doi-reference
The effect of machine learning explanations on user trust for automated diagnosis of covid-19
10.1016/j.compbiomed.2022.105587 · doi-reference
Explainable artificial intelligence (xai) for deep learning based medical imaging classification
10.3390/jimaging9090177 · doi-reference
An explainable ai system for medical image segmentation with preserved local resolution: mammogram tumor segmentation
10.1109/access.2023.3330465 · doi-reference
Explainable ai for glaucoma prediction analysis to understand risk factors in treatment planning
10.1109/tim.2022.3171613 · doi-reference
Ganterfactual—counterfactual explanations for medical non-experts using generative adversarial learning
10.3389/frai.2022.825565 · doi-reference
10.3390/app13063438
10.3390/app13063438 · doi-reference
Explainable ai for retinoblastoma diagnosis: interpreting deep learning models with lime and shap
10.3390/diagnostics13111932 · doi-reference
Systematic literature reviews in software engineering – a tertiary study
10.1016/j.infsof.2010.03.006 · doi-reference