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Found 11 papers, 5 papers with code
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 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.
LatentAugment: Data Augmentation via Guided Manipulation of GAN's Latent Space
Data Augmentation (DA) is a technique to increase the quantity and diversity of the training data, and by that alleviate overfitting and improve generalisation.
A Deep Learning Approach for Overall Survival Prediction in Lung Cancer with Missing Values
We present a novel approach to survival analysis with missing values in the context of NSCLC, which exploits the strengths of the transformer architecture to account only for available features without requiring any imputation strategy.
MATNet: Multi-Level Fusion Transformer-Based Model for Day-Ahead PV Generation Forecasting
It consists of a hybrid approach that combines the AI paradigm with the prior physical knowledge of PV power generation of physics-based methods.
A comparative study between paired and unpaired Image Quality Assessment in Low-Dose CT Denoising
To this end, we can use quantitative image quality assessment scores that we divided into two categories, i. e., paired and unpaired measures.
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.
RadioPathomics: Multimodal Learning in Non-Small Cell Lung Cancer for Adaptive Radiotherapy
In this work we therefore develop a multimodal late fusion approach that combines hand-crafted features computed from radiomics, pathomics and clinical data to predict radiation therapy treatment outcomes for non-small-cell lung cancer patients.
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
An Empirical Study on the Joint Impact of Feature Selection and Data Re-sampling on Imbalance Classification
To address this issue, we carry out a comprehensive empirical study on the joint influence of feature selection and re-sampling on two-class imbalance classification.
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.
