“Clinically and diagnostically relevant molecular alterations in salivary gland carcinomas”
PROF. ALENA SKÁLOVÁ
Coordination
FOP-UNICAMP
Prof. Dr. Pablo Agustin VargasAlena Skalova is professor of pathology at the Charles University in Prague, Faculty of Medicine in Plzen, Czech Republic. Author of the book Hellquist and Skalova: Histopathology of the Salivary Glands, Springer, 2014. Author of 7 chapters in WHO Classification of Tumours of Head and Neck (2017). Published 131 scientific papers in peer-reviewed international journals related to various topics in oral pathology especially involving salivary gland tumors.
"Novel Prognostic Factors in Early Stage Tongue Squamous Cell Carcinoma"
PROF. ILMO LEIVO
Coordination
FOUSP
Prof. Dr. Fábio Daumas NunesIlmo Leivo is professor and head of pathology at the University of Turku, Finland. Vice-President of Europe division of International Academy of Pathology (IAP). Honorary Member of the European Society for Pathology (ESP). Board Member of ESP Foundation and UEMS, Brussels. Published 175 international peer-reviewed publications on extracellular matrices and head and neck pathology, particularly salivary gland tumors and early tongue cancer.
"Emerging Machine Learning Tools for Head and Neck Cancer Characterization"
DR. ALEXANDER T. PEARSON
Coordination
FOP/UNICAMP
Prof. Dr. Alan Roger dos Santos SilvaMedical oncologist. Assistant Professor of Medicine, Section of Hematology/Oncology. Section of Computational Biomedicine and Biomedical Data Science. Co-Director, Head/Neck Cancer Program.The University of Chicago Medicine & Biological Sciences.His research combines laboratory experiments and mathematical models to more fully understand how head and neck cancers form and how to better design treatments for these cancers. He is currently the principal investigator on an NIH-funded study on the development of combination therapies for head and neck cancer.
Emerging Machine Learning Tools for Head and Neck Cancer Characterization
In this lecture, we will discuss oral cancer research using artificial intelligence based on computational neural networks. We introduce the central concepts of deep convolutional neural networks (DCNN) for automated medical image file processing, and discuss the factors which has led to dramatic increase in DCNN research over the last few years. We will also discuss potential applications of DCNNs in clinical care, and a survey of active research concepts. Finally, we will conclude with a description of pitfalls and opportunities for artificial intelligence in clinical oral cancer care.
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