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Found 8 papers, 1 papers with code
U-PET: MRI-based Dementia Detection with Joint Generation of Synthetic FDG-PET Images
To this end, we propose a multi-task method based on U-Net that takes T1-weighted MR images as an input to generate synthetic FDG-PET images and classifies the dementia progression of the patient into cognitive normal (CN), cognitive impairment (MCI), and AD.
U-GAT: Multimodal Graph Attention Network for COVID-19 Outcome Prediction
Specifically, we introduce a multimodal similarity metric to build a population graph for clustering patients and an image-based end-to-end Graph Attention Network to process this graph and predict the COVID-19 patient outcomes: admission to ICU, need for ventilation and mortality.
Simultaneous imputation and disease classification in incomplete medical datasets using Multigraph Geometric Matrix Completion (MGMC)
As a solution, we propose an end-to-end learning of imputation and disease prediction of incomplete medical datasets via Multigraph Geometric Matrix Completion (MGMC).
Domain-specific loss design for unsupervised physical training: A new approach to modeling medical ML solutions
The cataract, a developing opacity of the human eye lens, constitutes the world's most frequent cause for blindness.
Decision Support for Intoxication Prediction Using Graph Convolutional Networks
Due to the time-sensitive nature of these cases, doctors are required to propose a correct diagnosis and intervention within a minimal time frame.
Adaptive Image-Feature Learning for Disease Classification Using Inductive Graph Networks
We propose a new network architecture that exploits an inductive end-to-end learning approach for disease classification, where filters from both the CNN and the graph are trained jointly.
Multi-modal Graph Fusion for Inductive Disease Classification in Incomplete Datasets
Recently, several works proposed geometric deep learning approaches to solve disease classification, by modeling patients as nodes in a graph, along with graph signal processing of multi-modal features.
InceptionGCN: Receptive Field Aware Graph Convolutional Network for Disease Prediction
Geometric deep learning provides a principled and versatile manner for the integration of imaging and non-imaging modalities in the medical domain.
