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000163520 1001_ $$0P:(DE-2719)2810283$$aDyrba, Martin$$b0$$eFirst author
000163520 245__ $$aImproving 3D convolutional neural network comprehensibility via interactive visualization of relevance maps: evaluation in Alzheimer's disease.
000163520 260__ $$aLondon$$bBioMed Central$$c2021
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000163520 520__ $$aAlthough 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. We investigated whether models with higher accuracy also rely more on discriminative brain regions predefined by prior knowledge.We trained a CNN for the detection of AD in N = 663 T1-weighted MRI scans of patients with dementia and amnestic mild cognitive impairment (MCI) and verified the accuracy of the models via cross-validation and in three independent samples including in total N = 1655 cases. We evaluated the association of relevance scores and hippocampus volume to validate the clinical utility of this approach. To improve model comprehensibility, we implemented an interactive visualization of 3D CNN relevance maps, thereby allowing intuitive model inspection.Across the three independent datasets, group separation showed high accuracy for AD dementia versus controls (AUC ≥ 0.91) and moderate accuracy for amnestic MCI versus controls (AUC ≈ 0.74). Relevance maps indicated that hippocampal atrophy was considered the most informative factor for AD detection, with additional contributions from atrophy in other cortical and subcortical regions. Relevance scores within the hippocampus were highly correlated with hippocampal volumes (Pearson's r ≈ -0.86, p < 0.001).The relevance maps highlighted atrophy in regions that we had hypothesized a priori. This strengthens the comprehensibility of the CNN models, which were trained in a purely data-driven manner based on the scans and diagnosis labels. The high hippocampus relevance scores as well as the high performance achieved in independent samples support the validity of the CNN models in the detection of AD-related MRI abnormalities. The presented data-driven and hypothesis-free CNN modeling approach might provide a useful tool to automatically derive discriminative features for complex diagnostic tasks where clear clinical criteria are still missing, for instance for the differential diagnosis between various types of dementia.
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000163520 650_7 $$2Other$$aAlzheimer’s disease
000163520 650_7 $$2Other$$aConvolutional neural network
000163520 650_7 $$2Other$$aDeep learning
000163520 650_7 $$2Other$$aLayer-wise relevance propagation
000163520 650_7 $$2Other$$aMRI
000163520 650_2 $$2MeSH$$aAlzheimer Disease: diagnostic imaging
000163520 650_2 $$2MeSH$$aCognitive Dysfunction: diagnostic imaging
000163520 650_2 $$2MeSH$$aHumans
000163520 650_2 $$2MeSH$$aMagnetic Resonance Imaging: methods
000163520 650_2 $$2MeSH$$aNeural Networks, Computer
000163520 650_2 $$2MeSH$$aNeuroimaging: methods
000163520 693__ $$0EXP:(DE-2719)DELCODE-20140101$$5EXP:(DE-2719)DELCODE-20140101$$eLongitudinal Cognitive Impairment and Dementia Study$$x0
000163520 7001_ $$0P:(DE-2719)9001078$$aHanzig, Moritz$$b1
000163520 7001_ $$0P:(DE-2719)2811720$$aAltenstein, Slawek$$b2
000163520 7001_ $$0P:(DE-HGF)0$$aBader, Sebastian$$b3
000163520 7001_ $$0P:(DE-2719)9000476$$aBallarini, Tommaso$$b4
000163520 7001_ $$0P:(DE-2719)2810593$$aBrosseron, Frederic$$b5
000163520 7001_ $$0P:(DE-2719)2811351$$aBürger, Katharina$$b6$$udzne
000163520 7001_ $$0P:(DE-HGF)0$$aCantré, Daniel$$b7
000163520 7001_ $$0P:(DE-HGF)0$$aDechent, Peter$$b8
000163520 7001_ $$0P:(DE-2719)2811611$$aDobisch, Laura$$b9
000163520 7001_ $$0P:(DE-2719)2000005$$aDüzel, Emrah$$b10
000163520 7001_ $$0P:(DE-2719)9000543$$aEwers, Michael$$b11
000163520 7001_ $$0P:(DE-2719)2811326$$aFliessbach, Klaus$$b12
000163520 7001_ $$0P:(DE-2719)2811614$$aGlanz, Wenzel$$b13
000163520 7001_ $$0P:(DE-HGF)0$$aHaynes, John-Dylan$$b14
000163520 7001_ $$0P:(DE-2719)2000008$$aHeneka, Michael T$$b15
000163520 7001_ $$0P:(DE-HGF)0$$aJanowitz, Daniel$$b16
000163520 7001_ $$0P:(DE-2719)9001735$$aKeles, Deniz B$$b17
000163520 7001_ $$0P:(DE-2719)2810394$$aKilimann, Ingo$$b18
000163520 7001_ $$0P:(DE-2719)2000055$$aLaske, Christoph$$b19
000163520 7001_ $$0P:(DE-HGF)0$$aMaier, Franziska$$b20
000163520 7001_ $$0P:(DE-2719)9000443$$aMetzger, Coraline D$$b21
000163520 7001_ $$0P:(DE-2719)9001516$$aMunk, Matthias$$b22
000163520 7001_ $$0P:(DE-2719)2812234$$aPerneczky, Robert$$b23
000163520 7001_ $$0P:(DE-2719)2811024$$aPeters, Oliver$$b24
000163520 7001_ $$0P:(DE-2719)9000703$$aPreis, Lukas$$b25
000163520 7001_ $$0P:(DE-2719)2811122$$aPriller, Josef$$b26
000163520 7001_ $$0P:(DE-2719)9001808$$aRauchmann, Boris$$b27
000163520 7001_ $$0P:(DE-2719)2812341$$aRoy, Nina$$b28
000163520 7001_ $$0P:(DE-HGF)0$$aScheffler, Klaus$$b29
000163520 7001_ $$0P:(DE-2719)2812035$$aSchneider, Anja$$b30
000163520 7001_ $$0P:(DE-2719)2814326$$aSchott, Björn H$$b31
000163520 7001_ $$0P:(DE-2719)2811324$$aSpottke, Annika$$b32
000163520 7001_ $$0P:(DE-2719)2812446$$aSpruth, Eike J$$b33
000163520 7001_ $$0P:(DE-HGF)0$$aWeber, Marc-André$$b34
000163520 7001_ $$0P:(DE-HGF)0$$aErtl-Wagner, Birgit$$b35
000163520 7001_ $$0P:(DE-2719)2000057$$aWagner, Michael$$b36
000163520 7001_ $$0P:(DE-2719)2811317$$aWiltfang, Jens$$b37
000163520 7001_ $$0P:(DE-2719)2000032$$aJessen, Frank$$b38
000163520 7001_ $$0P:(DE-2719)2000026$$aTeipel, Stefan J$$b39$$eLast author
000163520 773__ $$0PERI:(DE-600)2506521-X$$a10.1186/s13195-021-00924-2$$gVol. 13, no. 1, p. 191$$n1$$p191$$tAlzheimer's research & therapy$$v13$$x1758-9193$$y2021
000163520 8564_ $$uhttps://doi.org/10.1186/s13195-021-00924-2
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