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Found 4 papers, 1 papers with code
Spectral Machine Learning for Pancreatic Mass Imaging Classification
Factors that influenced high performance of a well-designed integration of spectral learning and machine learning included: 1) use of eigenvectors corresponding to several of the largest eigenvalues of sample covariance matrix (spike eigenvectors) to choose input attributes in classification training, taking into account only the fundamental information of the raw images with less noise; 2) removal of irrelevant pixels based on mean-level spectral test to lower the challenges of memory capacity and enhance computational efficiency while maintaining superior classification accuracy; 3) adoption of state-of-the-art machine learning classification, gradient boosting and random forest.
Segmentation of Cardiac Structures via Successive Subspace Learning with Saab Transform from Cine MRI
Assessment of cardiovascular disease (CVD) with cine magnetic resonance imaging (MRI) has been used to non-invasively evaluate detailed cardiac structure and function.
Multi-task Federated Learning for Heterogeneous Pancreas Segmentation
Federated learning (FL) for medical image segmentation becomes more challenging in multi-task settings where clients might have different categories of labels represented in their data.
ConDistFL: Conditional Distillation for Federated Learning from Partially Annotated Data
Federated learning (FL) is a key technology enabling the collaborative development of a model without exchanging training data.
