Open AccessPosted Content
Explainable artificial intelligence (XAI) in deep learning-based medical image analysis.
TLDR
An overview of eXplainable Artificial Intelligence (XAI) used in deep learning-based medical image analysis is presented in this paper, where a framework of XAI criteria is introduced to classify deep learning based methods.Abstract:
With an increase in deep learning-based methods, the call for explainability of such methods grows, especially in high-stakes decision making areas such as medical image analysis. This survey presents an overview of eXplainable Artificial Intelligence (XAI) used in deep learning-based medical image analysis. A framework of XAI criteria is introduced to classify deep learning-based medical image analysis methods. Papers on XAI techniques in medical image analysis are then surveyed and categorized according to the framework and according to anatomical location. The paper concludes with an outlook of future opportunities for XAI in medical image analysis.read more
Citations
More filters
Journal ArticleDOI
Survey of Explainable AI Techniques in Healthcare
TL;DR: A survey of explainable AI techniques used in healthcare and related medical imaging applications can be found in this paper , where the authors provide guidelines to develop better interpretations of deep learning models using XAI concepts in medical image and text analysis.
Journal ArticleDOI
Explainable medical imaging AI needs human-centered design: guidelines and evidence from a systematic review
TL;DR: The INTRPRT guideline as mentioned in this paper suggests human-centered design principles, recommending formative user research as the first step to understand user needs and domain requirements, which increases the likelihood that the algorithms afford transparency and enable stakeholders to capitalize on the benefits of transparent ML.
Journal ArticleDOI
Explanatory classification of CXR images into COVID-19, Pneumonia and Tuberculosis using deep learning and XAI
TL;DR: In this paper , a deep learning model was proposed to predict four different pulmonary disorders (COVID-19, Pneumonia, and Tuberculosis) without compromising the classification accuracy and better feature extraction.
Journal ArticleDOI
Brain ageing in schizophrenia: evidence from 26 international cohorts via the ENIGMA Schizophrenia consortium
Constantinos Constantinides,Laura K.M. Han,Clara Alloza,Linda A. Antonucci,Celso Arango,Rosa Ayesa-Arriola,Nerisa Banaj,Alessandro Bertolini,Stefan Borgwardt,Jason M. Bruggemann,Juan R. Bustillo,Oleg Bykhovski,Vince D. Calhoun,Vaughan J. Carr,Stanley V. Catts,Young Chul Chung,Benedicto Crespo-Facorro,Covadonga M. Díaz-Caneja,Gary Donohoe,Stefan S. du Plessis,Jesse T. Edmond,Stefan Ehrlich,Robin Emsley,Lisa T. Eyler,Paola Fuentes-Claramonte,Foivos Georgiadis,Melissa J. Green,Amalia Guerrero-Pedraza,Minji Ha,Tim Hahn,Frans Henskens,Laurena Holleran,Stephanie Homan,Philipp Homan,Neda Jahanshad,Joost Janssen,Ellen Ji,Stefan Kaiser,Vasily Kaleda,Minah Kim,Woo-Sung Kim,Matthias Kirschner,Peter Kochunov,Yoo Bin Kwak,Jun Soo Kwon,Irina V. Lebedeva,Jingyu Liu,P Mitchie,Stijn Michielse,David Mothersill,Bryan J. Mowry,Victor Ortiz-García de la Foz,Christos Pantelis,Giulio Pergola,Fabrizio Piras,Edith Pomarol-Clotet,Adrian Preda,Yann Quidé,Paul E. Rasser,Kelly Rootes-Murdy,Raymond Salvador,M. Sangiuliano,Salvador Sarró,Ulrich Schall,André Schmidt,Rodney J. Scott,Pierluigi Selvaggi,Kang Sim,Antonin Skoch,Gianfranco Spalletta,Filip Spaniel,Sophia I. Thomopoulos,David Tomecek,Alexander Tomyshev,Diana Tordesillas-Gutiérrez,Therese van Amelsvoort,Javier Vázquez-Bourgon,Daniel James Vecchio,Aristotle N. Voineskos,Cynthia Shannon Weickert,Thomas W. Weickert,Paul M. Thompson,Lianne Schmaal,Theo G.M. van Erp,Jessica A. Turner,James H. Cole,Danai Dima,Esther Walton +87 more
TL;DR: In this paper , the authors investigated evidence for advanced brain ageing in adult SZ patients, and whether this was associated with clinical characteristics in a prospective meta-analytic study conducted by the ENIGMA Schizophrenia Working Group.
Journal ArticleDOI
Novel Insights in Spatial Epidemiology Utilizing Explainable AI (XAI) and Remote Sensing
Anastasios Temenos,Ioannis Tzortzis,Maria Kaselimi,Ioannis Rallis,Anastasios Doulamis,Nikolaos Doulamis +5 more
TL;DR: In this article , the authors proposed 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, socioeconomic, health, mobility and environmental factors all related to potential links with COVID-19.
References
More filters
Journal ArticleDOI
Long short-term memory
TL;DR: A novel, efficient, gradient based method called long short-term memory (LSTM) is introduced, which can learn to bridge minimal time lags in excess of 1000 discrete-time steps by enforcing constant error flow through constant error carousels within special units.
Proceedings Article
Very Deep Convolutional Networks for Large-Scale Image Recognition
Karen Simonyan,Andrew Zisserman +1 more
TL;DR: In this paper, the authors investigated the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting and showed that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 layers.
Journal ArticleDOI
Regression Shrinkage and Selection via the Lasso
TL;DR: A new method for estimation in linear models called the lasso, which minimizes the residual sum of squares subject to the sum of the absolute value of the coefficients being less than a constant, is proposed.
Proceedings ArticleDOI
Glove: Global Vectors for Word Representation
TL;DR: A new global logbilinear regression model that combines the advantages of the two major model families in the literature: global matrix factorization and local context window methods and produces a vector space with meaningful substructure.
Book ChapterDOI
Visualizing and Understanding Convolutional Networks
Matthew D. Zeiler,Rob Fergus +1 more
TL;DR: A novel visualization technique is introduced that gives insight into the function of intermediate feature layers and the operation of the classifier in large Convolutional Network models, used in a diagnostic role to find model architectures that outperform Krizhevsky et al on the ImageNet classification benchmark.
Related Papers (5)
A Survey on Explainable Artificial Intelligence (XAI): Toward Medical XAI
Erico Tjoa,Cuntai Guan +1 more