1,123 research outputs found
Recommended from our members
Deep learning for cardiac image segmentation: A review
Deep learning has become the most widely used approach for cardiac image segmentation in recent years. In this paper, we provide a review of over 100 cardiac image segmentation papers using deep learning, which covers common imaging modalities including magnetic resonance imaging (MRI), computed tomography (CT), and ultrasound (US) and major anatomical structures of interest (ventricles, atria and vessels). In addition, a summary of publicly available cardiac image datasets and code repositories are included to provide a base for encouraging reproducible research. Finally, we discuss the challenges and limitations with current deep learning-based approaches (scarcity of labels, model generalizability across different domains, interpretability) and suggest potential directions for future research
Dual-WGAN Ensemble Model for Alzheimer’s Dataset Augmentation with Minority Class Boosting
Copyright © 2023, IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Deep learning models have become very efficient and robust for several computer vision applications. However, to harness the benefits of state-of-art deep networks in the realm of disease detection and prediction, it is crucial that high-quality datasets be made available for the models to train on. This work recognizes the lack of training data (both in terms of quality and quantity of images) for using such networks for the detection of Alzheimer’s Disease. To address this issue, a Wasserstein Generative Adversarial Network (WGAN) is proposed to generate synthetic images for augmentation of an existing Alzheimer brain image dataset. The proposed approach is successful in generating high-quality images for inclusion in the Alzheimer image dataset, potentially making the dataset more suitable for training high-end models. This paper presents a two-fold contribution: (i) a WGAN is first developed for augmenting the non-dominant class (i.e. Moderate Demented) of the Alzheimer image dataset to bring the sample count (for that class) at par with the other classes, and (ii) another lightweight WGAN is used to augment the entire dataset for increasing the sample counts for all classes
BrainNetGAN:Data Augmentation of Brain Connectivity Using Generative Adversarial Network for Dementia Classification
Alzheimer's disease (AD) is the most common age-related dementia. It remains
a challenge to identify the individuals at risk of dementia for precise
management. Brain MRI offers a noninvasive biomarker to detect brain aging.
Previous evidence shows that the brain structural change detected by diffusion
MRI is associated with dementia. Mounting studies has conceptualised the brain
as a complex network, which has shown the utility of this approach in
characterising various neurological and psychiatric disorders. Therefore, the
structural connectivity shows promise in dementia classification. The proposed
BrainNetGAN is a generative adversarial network variant to augment the brain
structural connectivity matrices for binary dementia classification tasks.
Structural connectivity matrices between separated brain regions are
constructed using tractography on diffusion MRI data. The BrainNetGAN model is
trained to generate fake brain connectivity matrices, which are expected to
reflect latent distribution of the real brain network data. Finally, a
convolutional neural network classifier is proposed for binary dementia
classification. Numerical results show that the binary classification
performance in the testing set was improved using the BrainNetGAN augmented
dataset. The proposed methodology allows quick synthesis of an arbitrary number
of augmented connectivity matrices and can be easily transferred to similar
classification tasks.Comment: accepted by DGM4MICCAI workshop 202
- …