Vincenzo Della Mea

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Associate professor

Università degli Studi di Udine

Italy

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About

Vincenzo Della Mea is an Associate Professor at Università degli Studi di Udine, Italy. His research areas include emergency medical services, artificial intelligence applications in breast cancer, histopathology, and ontological modeling within health classifications. He has contributed significantly to recent publications on wastewater pollution detection, digital pathology workflows, and advanced natural language processing techniques.

Recent Grants

Grant: Close

A comprehensive CAD system based on radiologic- and pathologic-image biomarkers for diagnosis and prognosis of breast cancer relapse (BosomShield)

Open Date: 2022-01-01

Close Date: 2026-01-01

Articles (13)

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2024

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2024

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2022

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2021

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2021

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2020

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2020

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2020

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2020

Collaborators (6)

Mihai Horia Popescu

University of Udine

ITALY

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Daniel RACOCEANU

Professor

Sorbonne Université

FRANCE

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Gianni Cerro

Assistant Professor

University of Molise

ITALY

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Mario Molinara

Professore Aggregato

Università degli Studi di Cassino e del Lazio Meridionale

ITALY

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Luca Di Gaspero

Associate Professor

Università degli Studi di Udine

ITALY

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António Polónia

PORTUGAL

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Vincenzo Della Mea

Research Interests

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About

Recent Grants

Articles (13)

Supporting Fair and Efficient Emergency Medical Services in a Large Heterogeneous Region

Enhancing AI Research for Breast Cancer: A Comprehensive Review of Tumor-Infiltrating Lymphocyte Datasets

Optimizing Vision Transformers for Histopathology: Pretraining and Normalization in Breast Cancer Classification

Detection of Wastewater Pollution Through Natural Language Generation With a Low-Cost Sensing Platform

Harmonization of ICF Body Structures and ICD-11 Anatomic Detail: One foundation for multiple classifications

HEROHE Challenge: Predicting HER2 Status in Breast Cancer from Hematoxylin–Eosin Whole-Slide Imaging

Ontological modeling of the International Classification of Functioning, Disabilities and Health (ICF): Activities&Participation and Environmental Factors components

Best Practice Recommendations for the Implementation of a Digital Pathology Workflow in the Anatomic Pathology Laboratory by the European Society of Digital and Integrative Pathology (ESDIP)

DiLBERT: Cheap Embeddings for Disease Related Medical NLP

Underlying cause of death identification from death certificates using reverse coding to text and a NLP based deep learning approach

Detection of HER2 from Haematoxylin-Eosin Slides Through a Cascade of Deep Learning Classifiers via Multi-Instance Learning

Classification of Histologic Images Using a Single Staining: Experiments with Deep Learning on Deconvolved Images.

Logical Rules and a Preliminary Prototype for Translating Mortality Coding Rules from ICD-10 to ICD-11.

Collaborators (6)

Mihai Horia Popescu

Daniel RACOCEANU

Gianni Cerro

Mario Molinara

Luca Di Gaspero

António Polónia

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