82 citations · 380 across the 53 of their papers we have counts for
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MARIA: a Multimodal Transformer Model for Incomplete Healthcare Data
Camillo Maria Caruso, Paolo Soda, Valerio Guarrasi
In healthcare, the integration of multimodal data is pivotal for developing comprehensive diagnostic and predictive models. However, managing missing data remains a significant cha…
Class Balancing Diversity Multimodal Ensemble for Alzheimer's Disease Diagnosis and Early Detection
Arianna Francesconi, Lazzaro di Biase, Donato Cappetta +4
Alzheimer's disease (AD) poses significant global health challenges due to its increasing prevalence and associated societal costs. Early detection and diagnosis of AD are critical…
A Systematic Review of Intermediate Fusion in Multimodal Deep Learning for Biomedical Applications
Valerio Guarrasi, Fatih Aksu, Camillo Maria Caruso +4
Deep learning has revolutionized biomedical research by providing sophisticated methods to handle complex, high-dimensional data. Multimodal deep learning (MDL) further enhances th…
BodySLAM: A Generalized Monocular Visual SLAM Framework for Surgical Applications
G. Manni, C. Lauretti, F. Prata +3
Endoscopic surgery relies on two-dimensional views, posing challenges for surgeons in depth perception and instrument manipulation. While Monocular Visual Simultaneous Localization…
A Systematic Review on Long-Tailed Learning
Chongsheng Zhang, George Almpanidis, Gaojuan Fan +6
Long-tailed data is a special type of multi-class imbalanced data with a very large amount of minority/tail classes that have a very significant combined influence. Long-tailed lea…
Not Another Imputation Method: A Transformer-based Model for Missing Values in Tabular Datasets
Camillo Maria Caruso, Paolo Soda, Valerio Guarrasi
Handling missing values in tabular datasets presents a significant challenge in training and testing artificial intelligence models, an issue usually addressed using imputation tec…
Multi-Dataset Multi-Task Learning for COVID-19 Prognosis
Filippo Ruffini, Lorenzo Tronchin, Zhuoru Wu +4
In the fight against the COVID-19 pandemic, leveraging artificial intelligence to predict disease outcomes from chest radiographic images represents a significant scientific aim. T…
Multi-Scale Texture Loss for CT denoising with GANs
Francesco Di Feola, Lorenzo Tronchin, Valerio Guarrasi +1
Generative Adversarial Networks (GANs) have proved as a powerful framework for denoising applications in medical imaging. However, GAN-based denoising algorithms still suffer from…