3IA PhD/Postdoc Seminar #23

Published on February 24, 2023 Updated on March 2, 2023

on the March 3, 2023

from 10:30am to 12:00pm
Inria Sophia Antipolis


10:30 - 11:00
Thi Khuyen Le (Chair of O. Humbert)
Université Côte d'Azur

Comparison of handcrafted radiomics and 3D-CNN models to diagnose striatal dopamine deficiency in Parkinsonian syndromes based on 18F-FDOPA PET images

18F-FDOPA PET imaging is highly sensitive for detecting striatal presynaptic dopaminergic denervation and is used in clinical practice to assist the diagnosis of degenerative parkinsonian syndromes. Using 18F-FDOPA PET images, we investigated the respective performances of semi-automatic handcrafted radiomic and automatic 3D-CNN models to detect striatal dopaminergic denervation. We
demonstrated that the performance of the 3D-CNN model is high and stable (balanced accuracy = 99.18%) on an internal test set of PET images (n = 100 patients) acquired with a different PET scanner and a different imaging protocol from the training/validation set (n=417 patients). The 3D-CNN model correctly classifies PET images without the segmentation step required for the handcrafted radiomic model. It led to diagnostic performances similar to those of a junior medical expert. Furthermore, in a non-expert center, the 3D-CNN model allowed physicians to correctly reclassify 10 patients out of a cohort of 170 patients (6%), without introducing additional diagnostic errors.

10:30 - 11:00
Irene Balelli
External researcher
Inria, Epione team

The search for causality in the analysis and modeling of biomedical data

Current evolution in digital health heavily relies on data-driven machine learning and deep learning methods, which despite their impressive results, present some drawbacks related to 1) the need of a huge amount of curated data for training and 2) the lack of an underlined "causal" structure preventing the system to go beyond association. In this talk I will discuss the need of questioning the “How?” and the “Why?” when building data assimilation methods and predictive models, especially in the medical context, for more interpretable, explainable and trustable algorithmic prescriptions. I will present some helpful ingredients for this query and discuss some of my works, embedded in this philosophy.

11:30 - 12:00

Open discussion on the two contributions

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