Self-Supervised Pre-training Tasks for an fMRI Time-series Transformer in Autism Detection

• URL: http://arxiv.org/abs/2409.12304v1
• Date: Wed, 18 Sep 2024 20:29:23 GMT
• Title: Self-Supervised Pre-training Tasks for an fMRI Time-series Transformer in Autism Detection
• Authors: Yinchi Zhou, Peiyu Duan, Yuexi Du, Nicha C. Dvornek,
• Abstract summary: Autism Spectrum Disorder (ASD) is a neurodevelopmental condition that encompasses a wide variety of symptoms and degrees of impairment. We have developed a transformer-based self-supervised framework that directly analyzes time-series fMRI data without computing functional connectivity. We show that randomly masking entire ROIs gives better model performance than randomly masking time points in the pre-training step.
• Score: 3.665816629105171
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Autism Spectrum Disorder (ASD) is a neurodevelopmental condition that encompasses a wide variety of symptoms and degrees of impairment, which makes the diagnosis and treatment challenging. Functional magnetic resonance imaging (fMRI) has been extensively used to study brain activity in ASD, and machine learning methods have been applied to analyze resting state fMRI (rs-fMRI) data. However, fewer studies have explored the recent transformer-based models on rs-fMRI data. Given the superiority of transformer models in capturing long-range dependencies in sequence data, we have developed a transformer-based self-supervised framework that directly analyzes time-series fMRI data without computing functional connectivity. To address over-fitting in small datasets and enhance the model performance, we propose self-supervised pre-training tasks to reconstruct the randomly masked fMRI time-series data, investigating the effects of various masking strategies. We then finetune the model for the ASD classification task and evaluate it using two public datasets and five-fold cross-validation with different amounts of training data. The experiments show that randomly masking entire ROIs gives better model performance than randomly masking time points in the pre-training step, resulting in an average improvement of 10.8% for AUC and 9.3% for subject accuracy compared with the transformer model trained from scratch across different levels of training data availability. Our code is available on GitHub.

Related papers

• The effect of data augmentation and 3D-CNN depth on Alzheimer's Disease detection [51.697248252191265]
  This work summarizes and strictly observes best practices regarding data handling, experimental design, and model evaluation. We focus on Alzheimer's Disease (AD) detection, which serves as a paradigmatic example of challenging problem in healthcare. Within this framework, we train predictive 15 models, considering three different data augmentation strategies and five distinct 3D CNN architectures.
  arXiv  Detail & Related papers  (2023-09-13T10:40:41Z)
• Learning Sequential Information in Task-based fMRI for Synthetic Data Augmentation [10.629487323161323]
  We propose an approach for generating synthetic fMRI sequences that can be used to create augmented training datasets in downstream learning. The synthetic images are evaluated from multiple perspectives including visualizations and an autism spectrum disorder (ASD) classification task.
  arXiv  Detail & Related papers  (2023-08-29T18:36:21Z)
• Source-Free Collaborative Domain Adaptation via Multi-Perspective Feature Enrichment for Functional MRI Analysis [55.03872260158717]
  Resting-state MRI functional (rs-fMRI) is increasingly employed in multi-site research to aid neurological disorder analysis. Many methods have been proposed to reduce fMRI heterogeneity between source and target domains. But acquiring source data is challenging due to concerns and/or data storage burdens in multi-site studies. We design a source-free collaborative domain adaptation framework for fMRI analysis, where only a pretrained source model and unlabeled target data are accessible.
  arXiv  Detail & Related papers  (2023-08-24T01:30:18Z)
• Robust Fiber ODF Estimation Using Deep Constrained Spherical Deconvolution for Diffusion MRI [7.9283612449524155]
  A common practice to model the measured DW-MRI signal is via fiber orientation distribution function (fODF) measurement variabilities (e.g., inter- and intra-site variability, hardware performance, and sequence design) are inevitable during the acquisition of DW-MRI. Most existing model-based methods (e.g., constrained spherical deconvolution (CSD)) and learning based methods (e.g., deep learning (DL)) do not explicitly consider such variabilities in fODF modeling. We propose a novel data-driven deep constrained spherical deconvolution method to
  arXiv  Detail & Related papers  (2023-06-05T14:06:40Z)
• Convolutional Monge Mapping Normalization for learning on sleep data [63.22081662149488]
  We propose a new method called Convolutional Monge Mapping Normalization (CMMN) CMMN consists in filtering the signals in order to adapt their power spectrum density (PSD) to a Wasserstein barycenter estimated on training data. Numerical experiments on sleep EEG data show that CMMN leads to significant and consistent performance gains independent from the neural network architecture.
  arXiv  Detail & Related papers  (2023-05-30T08:24:01Z)
• Sequential Transfer Learning to Decode Heard and Imagined Timbre from fMRI Data [0.0]
  We present a sequential transfer learning framework for transformers on functional Magnetic Resonance Imaging (fMRI) data. In the first phase, we pre-train our stacked-encoder transformer architecture on Next Thought Prediction. In the second phase, we fine-tune the models and train additional fresh models on the supervised task of predicting whether or not two sequences of fMRI data were recorded while listening to the same musical timbre.
  arXiv  Detail & Related papers  (2023-05-22T16:58:26Z)
• Self-Supervised Pretraining on Paired Sequences of fMRI Data for Transfer Learning to Brain Decoding Tasks [0.0]
  We introduce a self-supervised pretraining framework for transformers on functional Magnetic Resonance Imaging (fMRI) data. First, we pretrain our architecture on two self-supervised tasks simultaneously to teach the model a general understanding of the temporal and spatial dynamics of human auditory cortex during music listening. Second, we finetune the pretrained models and train additional fresh models on a supervised fMRI classification task.
  arXiv  Detail & Related papers  (2023-05-15T22:53:12Z)
• 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)
• Pre-training and Fine-tuning Transformers for fMRI Prediction Tasks [69.85819388753579]
  TFF employs a transformer-based architecture and a two-phase training approach. Self-supervised training is applied to a collection of fMRI scans, where the model is trained for the reconstruction of 3D volume data. Results show state-of-the-art performance on a variety of fMRI tasks, including age and gender prediction, as well as schizophrenia recognition.
  arXiv  Detail & Related papers  (2021-12-10T18:04:26Z)
• Fader Networks for domain adaptation on fMRI: ABIDE-II study [68.5481471934606]
  We use 3D convolutional autoencoders to build the domain irrelevant latent space image representation and demonstrate this method to outperform existing approaches on ABIDE data.
  arXiv  Detail & Related papers  (2020-10-14T16:50:50Z)
• Improved Slice-wise Tumour Detection in Brain MRIs by Computing Dissimilarities between Latent Representations [68.8204255655161]
  Anomaly detection for Magnetic Resonance Images (MRIs) can be solved with unsupervised methods. We have proposed a slice-wise semi-supervised method for tumour detection based on the computation of a dissimilarity function in the latent space of a Variational AutoEncoder. We show that by training the models on higher resolution images and by improving the quality of the reconstructions, we obtain results which are comparable with different baselines.
  arXiv  Detail & Related papers  (2020-07-24T14:02:09Z)

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.