43 papers with code • 10 benchmarks • 13 datasets
Age Estimation is the task of estimating the age of a person from an image.
( Image credit: BridgeNet )
In many real-world prediction tasks, class labels include information about the relative ordering between labels, which is not captured by commonly-used loss functions such as multi-category cross-entropy.
It has been recently shown that Generative Adversarial Networks (GANs) can produce synthetic images of exceptional visual fidelity.
Residual representation learning simplifies the optimization problem of learning complex functions and has been widely used by traditional convolutional neural networks.
One of the main causes of unfair behavior in age prediction methods lies in the distribution and diversity of the training data.
Although impressive results have been achieved with conditional generative adversarial networks (cGANs), the existing cGANs-based methods typically use a single network to learn various aging effects between any two different age groups.
OTFPF: Optimal Transport-Based Feature Pyramid Fusion Network for Brain Age Estimation with 3D Overlapped ConvNeXt
In this paper, we propose an end-to-end neural network architecture, referred to as optimal transport based feature pyramid fusion (OTFPF) network, for the brain age estimation with T1 MRIs.
The proposed method employs a multi-task learning framework that regularizes the shared parameters of CNN and builds a synergy among different domains and tasks.