Search Results for author: Marc-André Schulz

Found 2 papers, 1 papers with code

DeepRepViz: Identifying Confounders in Deep Learning Model Predictions

1 code implementation27 Sep 2023 Roshan Prakash Rane, Jihoon Kim, Arjun Umesha, Didem Stark, Marc-André Schulz, Kerstin Ritter

In conclusion, the DeepRepViz framework provides a systematic approach to test for potential confounders such as age, sex, and imaging artifacts and improves the transparency of DL models for neuroimaging studies.

Deep Learning

Promises and pitfalls of deep neural networks in neuroimaging-based psychiatric research

no code implementations20 Jan 2023 Fabian Eitel, Marc-André Schulz, Moritz Seiler, Henrik Walter, Kerstin Ritter

By promising more accurate diagnostics and individual treatment recommendations, deep neural networks and in particular convolutional neural networks have advanced to a powerful tool in medical imaging.

Transfer Learning

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