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Challenges for cognitive decoding using deep learning methods
TL;DR: In this article, explainable artificial intelligence and transfer learning are used to improve the reproducibility and robustness of deep learning models for cognitive decoding, while also providing specific recommendations on how to improve robustness.
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Abstract: In cognitive decoding, researchers aim to characterize a brain region's representations by identifying the cognitive states (e.g., accepting/rejecting a gamble) that can be identified from the region's activity. Deep learning (DL) methods are highly promising for cognitive decoding, with their unmatched ability to learn versatile representations of complex data. Yet, their widespread application in cognitive decoding is hindered by their general lack of interpretability as well as difficulties in applying them to small datasets and in ensuring their reproducibility and robustness. We propose to approach these challenges by leveraging recent advances in explainable artificial intelligence and transfer learning, while also providing specific recommendations on how to improve the reproducibility and robustness of DL modeling results.
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Citations
Interpreting Brain Biomarkers: Challenges and solutions in interpreting machine learning-based predictive neuroimaging
01 Jul 2022
TL;DR: In this paper , the authors systematically review methods and applications for interpreting brain signatures derived from predictive neuroimaging, based on a survey of 326 research articles and discuss common issues in the existing literature and corresponding recommendations to address these pitfalls.
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Evaluating deep transfer learning for whole-brain cognitive decoding.
TL;DR: In this paper, transfer learning is applied to decode whole-brain functional Magnetic Resonance Imaging (fMRI) data to improve the performance of deep learning (DL) models with small numbers of samples.
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On the benefits of self-taught learning for brain decoding
TL;DR: In this article , a self-taught learning framework was proposed for improving brain decoding on new tasks. But the magnitude of the benefits strongly depends on the number of samples available both for pretraining and fine-tuning the models and on the complexity of the targeted downstream task.
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