Evaluating deep transfer learning for whole-brain cognitive decoding

• URL: http://arxiv.org/abs/2111.01562v1
• Date: Mon, 1 Nov 2021 15:44:49 GMT
• Title: Evaluating deep transfer learning for whole-brain cognitive decoding
• Authors: Armin W. Thomas and Ulman Lindenberger and Wojciech Samek and Klaus-Robert M\"uller
• Abstract summary: Transfer learning (TL) is well-suited to improve the performance of deep learning (DL) models in datasets with small numbers of samples. Here, we evaluate TL for the application of DL models to the decoding of cognitive states from whole-brain functional Magnetic Resonance Imaging (fMRI) data.
• Score: 11.898286908882561
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Research in many fields has shown that transfer learning (TL) is well-suited to improve the performance of deep learning (DL) models in datasets with small numbers of samples. This empirical success has triggered interest in the application of TL to cognitive decoding analyses with functional neuroimaging data. Here, we systematically evaluate TL for the application of DL models to the decoding of cognitive states (e.g., viewing images of faces or houses) from whole-brain functional Magnetic Resonance Imaging (fMRI) data. We first pre-train two DL architectures on a large, public fMRI dataset and subsequently evaluate their performance in an independent experimental task and a fully independent dataset. The pre-trained models consistently achieve higher decoding accuracies and generally require less training time and data than model variants that were not pre-trained, clearly underlining the benefits of pre-training. We demonstrate that these benefits arise from the ability of the pre-trained models to reuse many of their learned features when training with new data, providing deeper insights into the mechanisms giving rise to the benefits of pre-training. Yet, we also surface nuanced challenges for whole-brain cognitive decoding with DL models when interpreting the decoding decisions of the pre-trained models, as these have learned to utilize the fMRI data in unforeseen and counterintuitive ways to identify individual cognitive states.

Related papers

• NeuralOOD: Improving Out-of-Distribution Generalization Performance with Brain-machine Fusion Learning Framework [13.25912138698749]
  We propose a novel Brain-machine Fusion Learning framework to fuse visual knowledge from CV model and prior cognitive knowledge from the human brain. We employ a pre-trained visual neural encoding model to predict the functional Magnetic Resonance Imaging (fMRI) from visual features. Our model outperforms the DINOv2 and baseline models on the ImageNet-1k validation dataset as well as six curated OOD datasets.
  arXiv  Detail & Related papers  (2024-08-27T10:54:37Z)
• Diffusion-Based Neural Network Weights Generation [80.89706112736353]
  D2NWG is a diffusion-based neural network weights generation technique that efficiently produces high-performing weights for transfer learning. Our method extends generative hyper-representation learning to recast the latent diffusion paradigm for neural network weights generation. Our approach is scalable to large architectures such as large language models (LLMs), overcoming the limitations of current parameter generation techniques.
  arXiv  Detail & Related papers  (2024-02-28T08:34:23Z)
• Fantastic Gains and Where to Find Them: On the Existence and Prospect of General Knowledge Transfer between Any Pretrained Model [74.62272538148245]
  We show that for arbitrary pairings of pretrained models, one model extracts significant data context unavailable in the other. We investigate if it is possible to transfer such "complementary" knowledge from one model to another without performance degradation.
  arXiv  Detail & Related papers  (2023-10-26T17:59:46Z)
• Robustness and Generalization Performance of Deep Learning Models on Cyber-Physical Systems: A Comparative Study [71.84852429039881]
  Investigation focuses on the models' ability to handle a range of perturbations, such as sensor faults and noise. We test the generalization and transfer learning capabilities of these models by exposing them to out-of-distribution (OOD) samples.
  arXiv  Detail & Related papers  (2023-06-13T12:43:59Z)
• Efficiently Training Vision Transformers on Structural MRI Scans for Alzheimer's Disease Detection [2.359557447960552]
  Vision transformers (ViT) have emerged in recent years as an alternative to CNNs for several computer vision applications. We tested variants of the ViT architecture for a range of desired neuroimaging downstream tasks based on difficulty. We achieved a performance boost of 5% and 9-10% upon fine-tuning vision transformer models pre-trained on synthetic and real MRI scans.
  arXiv  Detail & Related papers  (2023-03-14T20:18:12Z)
• Leveraging the structure of dynamical systems for data-driven modeling [111.45324708884813]
  We consider the impact of the training set and its structure on the quality of the long-term prediction. We show how an informed design of the training set, based on invariants of the system and the structure of the underlying attractor, significantly improves the resulting models.
  arXiv  Detail & Related papers  (2021-12-15T20:09:20Z)
• Variational voxelwise rs-fMRI representation learning: Evaluation of sex, age, and neuropsychiatric signatures [0.0]
  We propose to apply non-linear representation learning to voxelwise rs-fMRI data. Learning the non-linear representations is done using a variational autoencoder (VAE) VAE is trained on voxelwise rs-fMRI data and performs non-linear dimensionality reduction that retains meaningful information.
  arXiv  Detail & Related papers  (2021-08-29T05:27:32Z)
• Transformer-Based Behavioral Representation Learning Enables Transfer Learning for Mobile Sensing in Small Datasets [4.276883061502341]
  We provide a neural architecture framework for mobile sensing data that can learn generalizable feature representations from time series. This architecture combines benefits from CNN and Trans-former architectures to enable better prediction performance.
  arXiv  Detail & Related papers  (2021-07-09T22:26:50Z)
• Gone Fishing: Neural Active Learning with Fisher Embeddings [55.08537975896764]
  There is an increasing need for active learning algorithms that are compatible with deep neural networks. This article introduces BAIT, a practical representation of tractable, and high-performing active learning algorithm for neural networks.
  arXiv  Detail & Related papers  (2021-06-17T17:26:31Z)
• Automatic Recall Machines: Internal Replay, Continual Learning and the Brain [104.38824285741248]
  Replay in neural networks involves training on sequential data with memorized samples, which counteracts forgetting of previous behavior caused by non-stationarity. We present a method where these auxiliary samples are generated on the fly, given only the model that is being trained for the assessed objective. Instead the implicit memory of learned samples within the assessed model itself is exploited.
  arXiv  Detail & Related papers  (2020-06-22T15:07:06Z)
• The Utility of Feature Reuse: Transfer Learning in Data-Starved Regimes [6.419457653976053]
  We describe a transfer learning use case for a domain with a data-starved regime. We evaluate the effectiveness of convolutional feature extraction and fine-tuning. We conclude that transfer learning enhances the performance of CNN architectures in data-starved regimes.
  arXiv  Detail & Related papers  (2020-02-29T18:48:58Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.