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Found 7 papers, 2 papers with code
Tackling Heterogeneity in Medical Federated learning via Vision Transformers
Optimization-based regularization methods have been effective in addressing the challenges posed by data heterogeneity in medical federated learning, particularly in improving the performance of underrepresented clients.
Computer-Vision Benchmark Segment-Anything Model (SAM) in Medical Images: Accuracy in 12 Datasets
Associations of SAM's accuracy with six factors were computed, independently and jointly, including segmentation difficulties as measured by segmentation ability score and by Dice overlap in U-Net, image dimension, size of the target region, image modality, and contrast.
U-Netmer: U-Net meets Transformer for medical image segmentation
The global-context information among local patches is learnt by the self-attention mechanism in Transformer and U-Net segments each local patch instead of flattening into tokens to solve the `token-flatten" problem.
Segmentation Ability Map: Interpret deep features for medical image segmentation
In addition, our method can provide a mean SA score which can give a performance estimation of the output on the test images without ground-truth.
Deep Relation Learning for Regression and Its Application to Brain Age Estimation
In this paper, we propose deep relation learning for regression, aiming to learn different relations between a pair of input images.
Global-Local Transformer for Brain Age Estimation
The fine-grained information from the local patches are fused with the global-context information by the attention mechanism, inspired by the transformer, to estimate the brain age.
Brain Age Estimation Using LSTM on Children's Brain MRI
Brain age prediction based on children's brain MRI is an important biomarker for brain health and brain development analysis.
