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SHR Neuro Krebs Kardio Lipid Stoffw Microb

Tinauer, C; Heber, S; Pirpamer, L; Damulina, A; Schmidt, R; Stollberger, R; Ropele, S; Langkammer, C.
Interpretable brain disease classification and relevance-guided deep learning.
SCI REP-UK. 2022; 12(1): 20254 Doi: 10.1038/s41598-022-24541-7 [OPEN ACCESS]
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Führende Autor*innen der Med Uni Graz
Langkammer Christian
Tinauer Christian Gerhard
Co-Autor*innen der Med Uni Graz
Damulina Anna
Heber Stefan
Pirpamer Lukas
Ropele Stefan
Schmidt Reinhold
Stollberger Rudolf

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Deep neural networks are increasingly used for neurological disease classification by MRI, but the networks' decisions are not easily interpretable by humans. Heat mapping by deep Taylor decomposition revealed that (potentially misleading) image features even outside of the brain tissue are crucial for the classifier's decision. We propose a regularization technique to train convolutional neural network (CNN) classifiers utilizing relevance-guided heat maps calculated online during training. The method was applied using T1-weighted MR images from 128 subjects with Alzheimer's disease (mean age = 71.9 ± 8.5 years) and 290 control subjects (mean age = 71.3 ± 6.4 years). The developed relevance-guided framework achieves higher classification accuracies than conventional CNNs but more importantly, it relies on less but more relevant and physiological plausible voxels within brain tissue. Additionally, preprocessing effects from skull stripping and registration are mitigated. With the interpretability of the decision mechanisms underlying CNNs, these results challenge the notion that unprocessed T1-weighted brain MR images in standard CNNs yield higher classification accuracy in Alzheimer's disease than solely atrophy.
Find related publications in this database (using NLM MeSH Indexing)
Humans - administration & dosage
Middle Aged - administration & dosage
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Aged, 80 and over - administration & dosage
Alzheimer Disease - diagnostic imaging
Deep Learning - administration & dosage
Head - administration & dosage
Brain - diagnostic imaging
Atrophy - administration & dosage

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