35 citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- Università Campus Bio-MedicoIT9 papers
- Umeå UniversitySE6 papers
- Centro Diagnostico ItalianoIT1 paper
- EntrustGB1 paper
- Ferrari (Italy)IT1 paper
- Marconi UniversityIT1 paper
- National University of Sciences and TechnologyPK1 paper
- San Raffaele University of RomeIT1 paper
- Shenzhen UniversityCN1 paper
- Università degli studi di Cassino e del Lazio MeridionaleIT1 paper
- University of Modena and Reggio EmiliaIT1 paper
- University of Rome Tor VergataIT1 paper
9 papers
Handling Missing Modalities in Multimodal Survival Prediction for Non-Small Cell Lung Cancer
Filippo Ruffini, Camillo Maria Caruso, Claudia Tacconi +16
Accurate survival prediction in Non-Small Cell Lung Cancer (NSCLC) requires integrating clinical, radiological, and histopathological data. Multimodal Deep Learning (MDL) can impro…
Multimodal Doctor-in-the-Loop: A Clinically-Guided Explainable Framework for Predicting Pathological Response in Non-Small Cell Lung Cancer
Alice Natalina Caragliano, Claudia Tacconi, Carlo Greco +7
This study proposes a novel approach combining Multimodal Deep Learning with intrinsic eXplainable Artificial Intelligence techniques to predict pathological response in non-small…
Doctor-in-the-Loop: An Explainable, Multi-View Deep Learning Framework for Predicting Pathological Response in Non-Small Cell Lung Cancer
Alice Natalina Caragliano, Filippo Ruffini, Carlo Greco +8
Non-small cell lung cancer (NSCLC) remains a major global health challenge, with high post-surgical recurrence rates underscoring the need for accurate pathological response predic…
Augmented Intelligence for Multimodal Virtual Biopsy in Breast Cancer Using Generative Artificial Intelligence
Aurora Rofena, Claudia Lucia Piccolo, Bruno Beomonte Zobel +2
Full-Field Digital Mammography (FFDM) is the primary imaging modality for routine breast cancer screening; however, its effectiveness is limited in patients with dense breast tissu…
XGeM: A Multi-Prompt Foundation Model for Multimodal Medical Data Generation
Daniele Molino, Francesco Di Feola, Eliodoro Faiella +5
The adoption of Artificial Intelligence in medical imaging holds great promise, yet it remains hindered by challenges such as data scarcity, privacy concerns, and the need for robu…
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 Deep Learning Approach for Virtual Contrast Enhancement in Contrast Enhanced Spectral Mammography
Aurora Rofena, Valerio Guarrasi, Marina Sarli +4
Contrast Enhanced Spectral Mammography (CESM) is a dual-energy mammographic imaging technique that first needs intravenously administration of an iodinated contrast medium; then, i…
A Deep Learning Approach for Overall Survival Prediction in Lung Cancer with Missing Values
Camillo Maria Caruso, Valerio Guarrasi, Sara Ramella +1
In the field of lung cancer research, particularly in the analysis of overall survival (OS), artificial intelligence (AI) serves crucial roles with specific aims. Given the prevale…
Lacking the embedding of a word? Look it up into a traditional dictionary
Elena Sofia Ruzzetti, Leonardo Ranaldi, Michele Mastromattei +2
Word embeddings are powerful dictionaries, which may easily capture language variations. However, these dictionaries fail to give sense to rare words, which are surprisingly often…