Explainable AI: A review of applications to neuroimaging data

Farahani, Farzad V.; Fiok, Krzysztof; Lahijanian, Behshad; Karwowski, Waldemar; Douglas, Pamela K.

doi:10.3389/fnins.2022.906290

Cited by 21 publications

(12 citation statements)

References 151 publications

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“…Last, the use of AI for imaging and analysis in gliomas is an extensive topic outside the scope of this guide and has been discussed in dedicated publications [209]. Our guide therefore only provides a snapshot of the current state-of-the-art of a continuously and rapidly evolving field.…”

Section: Discussionmentioning

confidence: 99%

A guide to advanced MRI processing for clinical glioma research

Clement,

Beun,

Arzanforoosh

et al. 2024

Preprint

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BackgroundTo date, multiple advanced magnetic resonance imaging (MRI) methods beyond conventional qualitative structural imaging for the diagnosis, prognosis, and treatment follow-up of glioma have demonstrated their utility for clinical studies. However, these methods often rely on complex off-scanner processing to yield the most information and to extract quantitative biomarkers, limiting their practical use for studies, as well as their clinical translation.While community-driven software solutions exist for these advanced MRI methods, many aspiring clinical researchers face challenges in acquiring the necessary knowledge to effectively apply these tools. This guide, an initiative of the Glioma MR imaging 2.0 network (GliMR), aims to provide an overview of existing solutions, communities, and repositories with the ultimate goal of enabling standardization, open science, and reproducible quantitative imaging studies of gliomas. Yet, most of the reviewed tools and approaches to image data analyses may also be used in the context of studies on diseases other than glioma.ContentThis guide summarizes the state-of-the-art processing software solutions and the repositories/communities for the following advanced MRI methods: DSC; DCE; ASL; diffusion MRI; relaxometry; MRF; MRS; CEST; SWI; QSM; MRE; and task-based and resting-state fMRI. For each of those, after a short introduction about the method and output parameters, the required and recommended image processing steps and quality control measures are described, and we point to further literature for more details. In addition, an overview of openly available software tools that provide these functionalities for MRI processing and exemplify workflows is given. Wherever possible, the readers are guided toward existing inventories, repositories, and communities, which offer not only a collection of these tools, but also more in-depth guidance. Each part concludes with an appraisal of the estimated required expertise and future development needs.ConclusionThis guide provides an extensive overview of the currently available processing tools that can help aspiring clinical researchers to obtain high-quality reproducible imaging data from advanced MRI scans of gliomas. While GliMR n is focused on glioma research, this guide will also be helpful for other clinical neuroimaging topics as general processing steps may not be specific to glioma only.

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Section: Discussionmentioning

confidence: 99%

A guide to advanced MRI processing for clinical glioma research

Clement,

Beun,

Arzanforoosh

et al. 2024

Preprint

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“…Numerous research efforts have delved into the application of XAI within medical imaging contexts, encompassing areas such as chest X-rays [20], CT scans [21], and MRI scans [22]. These studies have employed a range of XAI techniques, including but not limited to saliency maps, attribution maps, and decision trees.…”

Section: Related Workmentioning

confidence: 99%

Advancing Dermatological Diagnostics: Interpretable AI for Enhanced Skin Lesion Classification

Metta,

Beretta,

Guidotti

et al. 2024

Diagnostics

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A crucial challenge in critical settings like medical diagnosis is making deep learning models used in decision-making systems interpretable. Efforts in Explainable Artificial Intelligence (XAI) are underway to address this challenge. Yet, many XAI methods are evaluated on broad classifiers and fail to address complex, real-world issues, such as medical diagnosis. In our study, we focus on enhancing user trust and confidence in automated AI decision-making systems, particularly for diagnosing skin lesions, by tailoring an XAI method to explain an AI model’s ability to identify various skin lesion types. We generate explanations using synthetic images of skin lesions as examples and counterexamples, offering a method for practitioners to pinpoint the critical features influencing the classification outcome. A validation survey involving domain experts, novices, and laypersons has demonstrated that explanations increase trust and confidence in the automated decision system. Furthermore, our exploration of the model’s latent space reveals clear separations among the most common skin lesion classes, a distinction that likely arises from the unique characteristics of each class and could assist in correcting frequent misdiagnoses by human professionals.

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“…Stability examines how slight perturbations in the input affect the explanation provided by XAI techniques [107]. For example, Douglas and Farahani examined the stability of XAI performance for neuroimaging [108].…”

Section: Stabilitymentioning

confidence: 99%

Recent Advances in Explainable Artificial Intelligence for Magnetic Resonance Imaging

Qian

Wang

et al. 2023

Diagnostics

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Advances in artificial intelligence (AI), especially deep learning (DL), have facilitated magnetic resonance imaging (MRI) data analysis, enabling AI-assisted medical image diagnoses and prognoses. However, most of the DL models are considered as “black boxes”. There is an unmet need to demystify DL models so domain experts can trust these high-performance DL models. This has resulted in a sub-domain of AI research called explainable artificial intelligence (XAI). In the last decade, many experts have dedicated their efforts to developing novel XAI methods that are competent at visualizing and explaining the logic behind data-driven DL models. However, XAI techniques are still in their infancy for medical MRI image analysis. This study aims to outline the XAI applications that are able to interpret DL models for MRI data analysis. We first introduce several common MRI data modalities. Then, a brief history of DL models is discussed. Next, we highlight XAI frameworks and elaborate on the principles of multiple popular XAI methods. Moreover, studies on XAI applications in MRI image analysis are reviewed across the tissues/organs of the human body. A quantitative analysis is conducted to reveal the insights of MRI researchers on these XAI techniques. Finally, evaluations of XAI methods are discussed. This survey presents recent advances in the XAI domain for explaining the DL models that have been utilized in MRI applications.

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Explainable AI: A review of applications to neuroimaging data

Cited by 21 publications

References 151 publications

A guide to advanced MRI processing for clinical glioma research

A guide to advanced MRI processing for clinical glioma research

Advancing Dermatological Diagnostics: Interpretable AI for Enhanced Skin Lesion Classification

Recent Advances in Explainable Artificial Intelligence for Magnetic Resonance Imaging

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