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DeepRepViz: Identifying Confounders in Deep Learning Model Predictions
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.
Promises and pitfalls of deep neural networks in neuroimaging-based psychiatric research
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.
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