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Found 8 papers, 2 papers with code
Multi-Dataset Multi-Task Learning for COVID-19 Prognosis
In the fight against the COVID-19 pandemic, leveraging artificial intelligence to predict disease outcomes from chest radiographic images represents a significant scientific aim.
Multi-Scale Texture Loss for CT denoising with GANs
To grasp highly complex and non-linear textural relationships in the training process, this work presents a loss function that leverages the intrinsic multi-scale nature of the Gray-Level-Co-occurrence Matrix (GLCM).
A graph neural network-based model with Out-of-Distribution Robustness for enhancing Antiretroviral Therapy Outcome Prediction for HIV-1
We evaluated these models' robustness against Out-of-Distribution drugs in the test set, with a specific focus on the GNN's role in handling such scenarios.
A Deep Learning Approach for Virtual Contrast Enhancement in Contrast Enhanced Spectral Mammography
Contrast Enhanced Spectral Mammography (CESM) is a dual-energy mammographic imaging technique that first needs intravenously administration of an iodinated contrast medium; then, it collects both a low-energy image, comparable to standard mammography, and a high-energy image.
A Deep Learning Approach for Overall Survival Prediction in Lung Cancer with Missing Values
We compared our method with state-of-the-art models for survival analysis coupled with different imputation strategies.
Multimodal Explainability via Latent Shift applied to COVID-19 stratification
The explanation of the decision taken is computed by applying a latent shift that, simulates a counterfactual prediction revealing the features of each modality that contribute the most to the decision and a quantitative score indicating the modality importance.
Multi-objective optimization determines when, which and how to fuse deep networks: an application to predict COVID-19 outcomes
While deep learning has shown superior performance in several medical fields, in most of the cases it considers unimodal data only.
Explainable Artificial Intelligence (XAI)
Multimodal Deep Learning
AIforCOVID: predicting the clinical outcomes in patients with COVID-19 applying AI to chest-X-rays. An Italian multicentre study
Exhaustive evaluation shows promising performance both in 10-fold and leave-one-centre-out cross-validation, implying that clinical data and images have the potential to provide useful information for the management of patients and hospital resources.
