Title: Advanced Computational Techniques for Oncology: Towards Explainable Artificial Intelligence
Date: 13.01.2023
Venue: Congress Center of NCSR Demokritos, Athens, Greece
Organizer: BIOEMTECH
Program: Seminar’s program
Seminar’s Material: Download the Seminar’s Presentations & Videos
Fees: Free of charge
Registration: The registration is now closed
Contact: info@bioemtech.com
Dr. Panagiotis Papadimitroulas, PhD
Description:
This seminar aims to present advanced computational methodologies for oncology applications. The seminar is supported by the INFORM “Interpretability of Deep Neural Networks for Radiomics” project and provides an overview of state-of-the-art medical imaging tools in the field PET imaging. Invited speakers from University of Warsaw and LaTIM (INSERM) alongside with BIOEMTECH experts will cover the following topics:
General concepts of PET/CT in oncology – Radiomics extractiong and image processing techniques – Deep Learning in radiomics – Monte Carlo simulations and anthropomorphic models for medical imaging – Practical demonstration of GATE MC simulations – Machine Learning and Interpretability in Biology and Medicine – Deep Learning for medical imaging.
INFORM consortium investigates explainable artificial intelligence (XAI) with a dual aim of building high performance DNN-based classifiers and developing novel interpretability techniques for radiomics.
Program
09.45 – 10:00: Registration / Coffee
10.00 – 10:15: Welcome
10.15 – 11:00: PET/CT in oncology application and history of radiomics
(LaTIM – Mathieu Hatt)
11.00 – 11:45: Deep learning in radiomics applications
(LaTIM – Wistan Marchadour)
11.45 – 12:30: Coffee break
12.30 – 13:15: Monte Carlo simulations for medical imaging
(BIOEMTECH – Panagiotis Papadimitroulas)
13.15 – 14:00: GATE MC simulations – Demonstration / Examples / Practical
(BIOEMTECH – George Savvidis)
14.00 – 15:00: Lunch break
15.00 – 15:45: Machine Learning and Interpretability in Biology and Medicine
(UW – Neo Christopher Chung)
15.45 – 16:15: Coffee break
16.15 – 17:00: Interpretable Deep Learning for Medical Images
(UW – Lennart Brocki)
17.00 – 17:30: Q&A – General discussion – Conclusions
