Improving 3D convolutional neural network comprehensibility via interactive visualization of relevance maps: evaluation in Alzheimer’s disease

  title={Improving 3D convolutional neural network comprehensibility via interactive visualization of relevance maps: evaluation in Alzheimer’s disease},
  author={Martin Dyrba and Moritz Hanzig and Slawek Altenstein and Sebastian Bader and Tommaso Ballarini and Frederic Brosseron and Katharina Buerger and Daniel Cantr{\'e} and Peter Dechent and Laura Dobisch and Emrah D{\"u}zel and Michael Ewers and Klaus Fliessbach and Wenzel Glanz and John-Dylan Haynes and Michael T. Heneka and Daniel Janowitz and Deniz Baris Keles and Ingo Kilimann and Christoph Laske and Franziska Maier and Coraline D. Metzger and Matthias H. J. Munk and Robert Perneczky and Oliver Peters and Lukas Preis and Josef Priller and Boris Stephan Rauchmann and Nina Roy and Klaus Scheffler and Anja Schneider and Bj{\"o}rn H. Schott and Annika Spottke and Eike J. Spruth and Marc-Andr{\'e} Weber and Birgit B. Ertl-Wagner and Michael Wagner and Jens Wiltfang and Frank Jessen and Stefan J. Teipel},
  journal={Alzheimer's Research \& Therapy},
Background Although convolutional neural networks (CNNs) achieve high diagnostic accuracy for detecting Alzheimer’s disease (AD) dementia based on magnetic resonance imaging (MRI) scans, they are not yet applied in clinical routine. One important reason for this is a lack of model comprehensibility. Recently developed visualization methods for deriving CNN relevance maps may help to fill this gap as they allow the visualization of key input image features that drive the decision of the model… 

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