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Found 8 papers, 3 papers with code
Data Collaboration Analysis applied to Compound Datasets and the Introduction of Projection data to Non-IID settings
However, federated learning is encumbered by low accuracy in not identically and independently distributed (non-IID) settings, i. e., data partitioning has a large label bias, and is considered unsuitable for compound datasets, which tend to have large label bias.
Achieving Transparency in Distributed Machine Learning with Explainable Data Collaboration
Transparency of Machine Learning models used for decision support in various industries becomes essential for ensuring their ethical use.
Anomaly-aware multiple instance learning for rare anemia disorder classification
Deep learning-based classification of rare anemia disorders is challenged by the lack of training data and instance-level annotations.
Sickle Cell Disease Severity Prediction from Percoll Gradient Images using Graph Convolutional Networks
Sickle cell disease (SCD) is a severe genetic hemoglobin disorder that results in premature destruction of red blood cells.
Fourier Transform of Percoll Gradients Boosts CNN Classification of Hereditary Hemolytic Anemias
Hereditary hemolytic anemias are genetic disorders that affect the shape and density of red blood cells.
Accuracy and Privacy Evaluations of Collaborative Data Analysis
Distributed data analysis without revealing the individual data has recently attracted significant attention in several applications.
Federated Learning System without Model Sharing through Integration of Dimensional Reduced Data Representations
Dimensionality Reduction is a commonly used element in a machine learning pipeline that helps to extract important features from high-dimensional data.
Attention based Multiple Instance Learning for Classification of Blood Cell Disorders
In blood cell disorders, only a subset of all cells is morphologically altered and relevant for the diagnosis.
