"V体育ios版" Call for papers - Digital pathology

Germán Corredor, PhD, Emory University, USA

Dr Germán Corredor is Assistant Professor in the Wallace H. Coulter Department of Biomedical Engineering at Emory University and Georgia Institute of Technology, USA. He is also a member of the Cancer Immunology Research Program at the Winship Cancer Institute of Emory University in Atlanta. Dr Corredor earned his PhD in Electrical Engineering, MSc in Computer Engineering, and BS in Computer Engineering from the Universidad Nacional de Colombia, Colombia VSports手机版. His doctoral research focused on developing an end-to-end pipeline for pathology, including strategies and user interfaces for providing pathologists with efficient access to visual content and extracting diagnostic and prognostic information from histopathology images of various cancers, such as ductal carcinoma in situ, ER+ breast cancer, non-small cell lung cancer, basal cell carcinoma, and gastric adenocarcinoma. Dr Corredor's research focuses on developing innovative imaging biomarkers to predict diagnoses, outcomes, and treatment responses for various diseases. He is actively engaged in computational pathology, working to automatically identify key diagnostic regions, determine which patients are likely to have poor prognoses, and identify those who might benefit from specific treatments.

Thomas Tavolara, PhD, Mayo Clinic, USA Dr Thomas Tavolara is a researcher at the intersection of computational pathology and artificial intelligence, focused on leveraging machine learning to advance disease diagnostics and treatment stratification. With degrees in computer science, microbiology, and biomedical engineering, his work bridges technical innovation and biological insight. His research centers on the development of deep learning models for histopathological image analysis, aiming to classify disease subtypes, predict treatment responses, and identify clinically meaningful biomarkers V体育安卓版. He has a particular interest in weakly supervised and self-supervised learning techniques, which allow for effective model training in the face of limited labeled data—a common challenge in medical imaging. Dr Tavolara also explores the integration of clinical data with imaging to enhance model interpretability and predictive power. More recently, he has begun investigating the role of large language models (LLMs) in structuring and generating laboratory and pathology reports, with the goal of improving scalability, standardization, and integration across clinical systems. A major theme of his work is the creation of AI-driven digital assays that offer reproducible, interpretable, and cost-effective alternatives to traditional diagnostics. These tools are designed to support precision medicine by reducing variability, enhancing diagnostic accuracy, and informing individualized treatment strategies. Dr Tavolara’s overarching goal is to develop AI systems that not only perform at a high level but also align with clinical workflows—advancing both the science and practice of pathology.