Explainable artificial intelligence (XAI) in deep learning-based medical image analysis

Velden, Bas H. M. van der; Kuijf, Hugo J.; Gilhuijs, Kenneth G. A.; Viergever, Max A.

doi:10.1016/j.media.2022.102470

Cited by 454 publications

(220 citation statements)

References 150 publications

Supporting

Mentioning

153

Contrasting

Order By: Relevance

“…Understanding how an AI-based CAD scheme or prediction model can make reliable prediction is non-trivial to most individuals because it is very difficult to explain the clinical or physical meanings of the features automatically extracted by a CNN-based deep transfer learning model. Thus, developing explainable AI models in medical image analysis has emerged as a hot research topic ( 150 ). Among these efforts, visualization tools with interactive capability or functions have been developed that aim to show the user what regions in an image or image patterns (i.e., “heat maps”) contribute the most to the decision made by AI models ( 151 , 152 ).…”

Section: Discussion – Outlook and Challengesmentioning

confidence: 99%

Applying artificial intelligence technology to assist with breast cancer diagnosis and prognosis prediction

et al. 2022

View full text Add to dashboard Cite

Breast cancer remains the most diagnosed cancer in women. Advances in medical imaging modalities and technologies have greatly aided in the early detection of breast cancer and the decline of patient mortality rates. However, reading and interpreting breast images remains difficult due to the high heterogeneity of breast tumors and fibro-glandular tissue, which results in lower cancer detection sensitivity and specificity and large inter-reader variability. In order to help overcome these clinical challenges, researchers have made great efforts to develop computer-aided detection and/or diagnosis (CAD) schemes of breast images to provide radiologists with decision-making support tools. Recent rapid advances in high throughput data analysis methods and artificial intelligence (AI) technologies, particularly radiomics and deep learning techniques, have led to an exponential increase in the development of new AI-based models of breast images that cover a broad range of application topics. In this review paper, we focus on reviewing recent advances in better understanding the association between radiomics features and tumor microenvironment and the progress in developing new AI-based quantitative image feature analysis models in three realms of breast cancer: predicting breast cancer risk, the likelihood of tumor malignancy, and tumor response to treatment. The outlook and three major challenges of applying new AI-based models of breast images to clinical practice are also discussed. Through this review we conclude that although developing new AI-based models of breast images has achieved significant progress and promising results, several obstacles to applying these new AI-based models to clinical practice remain. Therefore, more research effort is needed in future studies.

show abstract

Section: Discussion – Outlook and Challengesmentioning

confidence: 99%

Applying artificial intelligence technology to assist with breast cancer diagnosis and prognosis prediction

et al. 2022

View full text Add to dashboard Cite

show abstract

“…This information can help technical industrial teams understand potential risk factors for OHPPs and identify warning signs of the early stages of musculoskeletal disorders and how to cope with work-related absenteeism [ 57 ]. In the future, we will try to use other remaining XAI techniques [ 58 , 59 , 60 ] in our model for further improvement. Different XAI techniques will help us to make our model more explainable to others.…”

Section: Discussionmentioning

confidence: 99%

Human-Centered Explainable Artificial Intelligence: Automotive Occupational Health Protection Profiles in Prevention Musculoskeletal Symptoms

Mollaei

Fujão²,

Silva

et al. 2022

IJERPH

View full text Add to dashboard Cite

In automotive and industrial settings, occupational physicians are responsible for monitoring workers’ health protection profiles. Workers’ Functional Work Ability (FWA) status is used to create Occupational Health Protection Profiles (OHPP). This is a novel longitudinal study in comparison with previous research that has predominantly relied on the causality and explainability of human-understandable models for industrial technical teams like ergonomists. The application of artificial intelligence can support the decision-making to go from a worker’s Functional Work Ability to explanations by integrating explainability into medical (restriction) and support in contexts of individual, work-related, and organizational risk conditions. A sample of 7857 for the prognosis part of OHPP based on Functional Work Ability in the Portuguese language in the automotive industry was taken from 2019 to 2021. The most suitable regression models to predict the next medical appointment for the workers’ body parts protection were the models based on CatBoost regression, with an RMSLE of 0.84 and 1.23 weeks (mean error), respectively. CatBoost algorithm is also used to predict the next body part severity of OHPP. This information can help our understanding of potential risk factors for OHPP and identify warning signs of the early stages of musculoskeletal symptoms and work-related absenteeism.

show abstract

“…This is one of the reasons why it is essential to develop proper mechanisms to explain the decisions of AI models. Recently, there have been various works published, such as [33][34][35][36], where the explanations are provided by the proposed frameworks in a variety of target-applications.…”

Section: Explainable Ai Frameworkmentioning

confidence: 99%

Novel Insights in Spatial Epidemiology Utilizing Explainable AI (XAI) and Remote Sensing

et al. 2022

View full text Add to dashboard Cite

The COVID-19 pandemic has affected many aspects of human life around the world, due to its tremendous outcomes on public health and socio-economic activities. Policy makers have tried to develop efficient responses based on technologies and advanced pandemic control methodologies, to limit the wide spreading of the virus in urban areas. However, techniques such as social isolation and lockdown are short-term solutions that minimize the spread of the pandemic in cities and do not invert long-term issues that derive from climate change, air pollution and urban planning challenges that enhance the spreading ability. Thus, it seems crucial to understand what kind of factors assist or prevent the wide spreading of the virus. Although AI frameworks have a very efficient predictive ability as data-driven procedures, they often struggle to identify strong correlations among multidimensional data and provide robust explanations. In this paper, we propose the fusion of a heterogeneous, spatio-temporal dataset that combine data from eight European cities spanning from 1 January 2020 to 31 December 2021 and describe atmospheric, socio-economic, health, mobility and environmental factors all related to potential links with COVID-19. Remote sensing data are the key solution to monitor the availability on public green spaces between cities in the study period. So, we evaluate the benefits of NIR and RED bands of satellite images to calculate the NDVI and locate the percentage in vegetation cover on each city for each week of our 2-year study. This novel dataset is evaluated by a tree-based machine learning algorithm that utilizes ensemble learning and is trained to make robust predictions on daily cases and deaths. Comparisons with other machine learning techniques justify its robustness on the regression metrics RMSE and MAE. Furthermore, the explainable frameworks SHAP and LIME are utilized to locate potential positive or negative influence of the factors on global and local level, with respect to our model’s predictive ability. A variation of SHAP, namely treeSHAP, is utilized for our tree-based algorithm to make fast and accurate explanations.

show abstract

Explainable artificial intelligence (XAI) in deep learning-based medical image analysis

Cited by 454 publications

References 150 publications

Applying artificial intelligence technology to assist with breast cancer diagnosis and prognosis prediction

Applying artificial intelligence technology to assist with breast cancer diagnosis and prognosis prediction

Human-Centered Explainable Artificial Intelligence: Automotive Occupational Health Protection Profiles in Prevention Musculoskeletal Symptoms

Novel Insights in Spatial Epidemiology Utilizing Explainable AI (XAI) and Remote Sensing

Contact Info

Product

Resources

About