Search Results for author:
Found 5 papers, 1 papers with code
MaxCorrMGNN: A Multi-Graph Neural Network Framework for Generalized Multimodal Fusion of Medical Data for Outcome Prediction
With the emergence of multimodal electronic health records, the evidence for an outcome may be captured across multiple modalities ranging from clinical to imaging and genomic data.
Fusing Modalities by Multiplexed Graph Neural Networks for Outcome Prediction in Tuberculosis
All these nuances make simple methods of early, late, or intermediate fusion of features inadequate for outcome prediction.
Learning-based Defect Recognition for Quasi-Periodic Microscope Images
Controlling crystalline material defects is crucial, as they affect properties of the material that may be detrimental or beneficial for the final performance of a device.
Towards Explainable Graph Representations in Digital Pathology
Explainability of machine learning (ML) techniques in digital pathology (DP) is of great significance to facilitate their wide adoption in clinics.
Mitosis Detection Under Limited Annotation: A Joint Learning Approach
Our framework significantly improves the detection with small training data and achieves on par or superior performance compared to state-of-the-art methods for using the entire training data.
