Does pre-training on brain-related tasks results in better deep-learning-based brain age biomarkers?

• URL: http://arxiv.org/abs/2307.05241v1
• Date: Tue, 11 Jul 2023 13:16:04 GMT
• Title: Does pre-training on brain-related tasks results in better deep-learning-based brain age biomarkers?
• Authors: Bruno Machado Pacheco, Victor Hugo Rocha de Oliveira, Augusto Braga Fernandes Antunes, Saulo Domingos de Souza Pedro, and Danilo Silva
• Abstract summary: We investigate the impact of a pre-training step on deep learning models for brain age prediction. We validate the resulting brain age biomarker on images of patients with mild cognitive impairment and Alzheimer's disease.
• Score: 4.114671069824331
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Brain age prediction using neuroimaging data has shown great potential as an indicator of overall brain health and successful aging, as well as a disease biomarker. Deep learning models have been established as reliable and efficient brain age estimators, being trained to predict the chronological age of healthy subjects. In this paper, we investigate the impact of a pre-training step on deep learning models for brain age prediction. More precisely, instead of the common approach of pre-training on natural imaging classification, we propose pre-training the models on brain-related tasks, which led to state-of-the-art results in our experiments on ADNI data. Furthermore, we validate the resulting brain age biomarker on images of patients with mild cognitive impairment and Alzheimer's disease. Interestingly, our results indicate that better-performing deep learning models in terms of brain age prediction on healthy patients do not result in more reliable biomarkers.

Related papers

• Dual Graph Attention based Disentanglement Multiple Instance Learning for Brain Age Estimation [26.33669583527592]
  We propose a Dual Graph Attention based Disentanglement Multi-instance Learning (DGA-DMIL) framework for improving brain age estimation. A dual graph attention aggregator is then proposed to learn the backbone features by exploiting the intra- and inter-instance relationships. Our proposed model demonstrates exceptional accuracy in estimating brain age, achieving a remarkable mean absolute error of 2.12 years in the UK Biobank.
  arXiv  Detail & Related papers  (2024-03-02T16:13:06Z)
• Towards a Foundation Model for Brain Age Prediction using coVariance Neural Networks [102.75954614946258]
  Increasing brain age with respect to chronological age can reflect increased vulnerability to neurodegeneration and cognitive decline. NeuroVNN is pre-trained as a regression model on healthy population to predict chronological age. NeuroVNN adds anatomical interpretability to brain age and has a scale-free' characteristic that allows its transference to datasets curated according to any arbitrary brain atlas.
  arXiv  Detail & Related papers  (2024-02-12T14:46:31Z)
• Explainable Brain Age Prediction using coVariance Neural Networks [94.81523881951397]
  We propose an explanation-driven and anatomically interpretable framework for brain age prediction using cortical thickness features. Specifically, our brain age prediction framework extends beyond the coarse metric of brain age gap in Alzheimer's disease (AD) We make two important observations: VNNs can assign anatomical interpretability to elevated brain age gap in AD by identifying contributing brain regions.
  arXiv  Detail & Related papers  (2023-05-27T22:28:25Z)
• Deep Learning for Brain Age Estimation: A Systematic Review [41.292656643344294]
  Machine Learning models have been successfully employed on neuroimaging data for accurately predicting brain age. Deep neural networks (also referred to as deep learning) have become prevalent in manifold neuroimaging studies.
  arXiv  Detail & Related papers  (2022-12-07T15:19:59Z)
• Neural Language Models are not Born Equal to Fit Brain Data, but Training Helps [75.84770193489639]
  We examine the impact of test loss, training corpus and model architecture on the prediction of functional Magnetic Resonance Imaging timecourses of participants listening to an audiobook. We find that untrained versions of each model already explain significant amount of signal in the brain by capturing similarity in brain responses across identical words. We suggest good practices for future studies aiming at explaining the human language system using neural language models.
  arXiv  Detail & Related papers  (2022-07-07T15:37:17Z)
• Voxel-level Importance Maps for Interpretable Brain Age Estimation [70.5330922395729]
  We focus on the task of brain age regression from 3D brain Magnetic Resonance (MR) images using a Convolutional Neural Network, termed prediction model. We implement a noise model which aims to add as much noise as possible to the input without harming the performance of the prediction model. We test our method on 13,750 3D brain MR images from the UK Biobank, and our findings are consistent with the existing neuropathology literature.
  arXiv  Detail & Related papers  (2021-08-11T18:08:09Z)
• Brain Age Estimation From MRI Using Cascade Networks with Ranking Loss [75.03117866578913]
  A novel 3D convolutional network, called two-stage-age-network (TSAN), is proposed to estimate brain age from T1-weighted MRI data. Experiments with $6586$ MRIs showed that TSAN could provide accurate brain age estimation.
  arXiv  Detail & Related papers  (2021-06-06T07:11:25Z)
• Patch-based Brain Age Estimation from MR Images [64.66978138243083]
  Brain age estimation from Magnetic Resonance Images (MRI) derives the difference between a subject's biological brain age and their chronological age. Early detection of neurodegeneration manifesting as a higher brain age can potentially facilitate better medical care and planning for affected individuals. We develop a new deep learning approach that uses 3D patches of the brain as well as convolutional neural networks (CNNs) to develop a localised brain age estimator.
  arXiv  Detail & Related papers  (2020-08-29T11:50:37Z)

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.