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Found 7 papers, 4 papers with code
Meta Transfer of Self-Supervised Knowledge: Foundation Model in Action for Post-Traumatic Epilepsy Prediction
To explore the generalizability of the foundation model in downstream applications, we then apply the model to an unseen TBI dataset for prediction of PTE using zero-shot learning.
Neuro-GPT: Towards A Foundation Model for EEG
To handle the scarcity and heterogeneity of electroencephalography (EEG) data for Brain-Computer Interface (BCI) tasks, and to harness the power of large publicly available data sets, we propose Neuro-GPT, a foundation model consisting of an EEG encoder and a GPT model.
SemST: Semantically Consistent Multi-Scale Image Translation via Structure-Texture Alignment
Unsupervised image-to-image (I2I) translation learns cross-domain image mapping that transfers input from the source domain to output in the target domain while preserving its semantics.
Toward Improved Generalization: Meta Transfer of Self-supervised Knowledge on Graphs
Transferring knowledge from a source domain with abundant training data to a target domain is effective for improving representation learning on scarce training data.
Learning from imperfect training data using a robust loss function: application to brain image segmentation
Segmentation is one of the most important tasks in MRI medical image analysis and is often the first and the most critical step in many clinical applications.
Semi-supervised Learning using Robust Loss
The amount of manually labeled data is limited in medical applications, so semi-supervised learning and automatic labeling strategies can be an asset for training deep neural networks.
Semi-Supervised Brain Lesion Segmentation with an Adapted Mean Teacher Model
Since unannotated data is generally abundant, it is desirable to use unannotated data to improve the segmentation performance for CNNs when limited annotated data is available.
