Deep Learning for Quality Control of Subcortical Brain 3D Shape Models

Shape matching with medial curves and 1-d group-wise registration

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Medial demons registration localizes the degree of genetic influence over subcortical shape variability: An n= 1480 meta-analysis

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Subcortical brain volume abnormalities in 2028 individuals with schizophrenia and 2540 healthy controls via the ENIGMA consortium

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Deep residual learning for image recognition

He, K., Zhang, X., Ren, S., Sun, J.: · 2016

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Grad-cam: Visual explanations from deep networks via gradient-based localization

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Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: · 2016

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Harmonized large-scale anatomical shape analysis: Mapping subcortical differences across the ENIGMA Bipolar, Schizophrenia, and Major Depression working groups

Gutman, B., Ching, C., Andreassen, O., Schmaal, L., Veltman, D., Van Erp, T., Turner, J., Thompson, P.M., et al.: · 2017

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Inception-v4, inception-resnet and the impact of residual connections on learning

Szegedy, C., Ioffe, S., Vanhoucke, V., Alemi, A.A.: · 2017

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Measuring the tendency of cnns to learn surface statistical regularities

Original

Jo, J., Bengio, Y.: · 2017

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Visualizing deep neural network decisions: Prediction difference analysis

Original

Zintgraf, L.M., Cohen, T.S., Adel, T., Welling, M.: · 2017

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Cortical abnormalities in adults and adolescents with major depression based on brain scans from 20 cohorts worldwide in the enigma major depressive disorder working group

Schmaal, L., Hibar, D.P., et al.: · 2017

Later among the works it cites.

Spherical cnns

Original

Cohen, T.S., Geiger, M., Koehler, J., Welling, M.: · 2018

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