Uncertainty Quantification in Medical Image Analysis

MICCAI 2024 Tutorial

2nd edition

7 October 2024

( Day 2 of conference, morning or afternoon TBC)

About

This MICCAI 2024 half-day event aims to foster discussions within the scientific community regarding the trustworthiness of deep learning model predictions by providing valuable insights into uncertainty quantification and model calibration techniques necessary for making reliable decisions.

This second edition of the tutorial will be focused on the topic of uncertainty quantification, model calibration, and conformal prediction. This year's tutorial is conducted in a joint event with the UNSURE workshop, combining talks from experts, hands-on materials, modern research presentation, keynote presentation, and a panel discussion.

Participants will have an opportunity to gain a comprehensive understanding of uncertainty quantification, covering mathematical foundations and cutting-edge research questions while acquiring practical tools for deriving state-of-the-art uncertainty estimates in medical image analysis.

We believe this tutorial is an essential resource for scientists, clinicians, and industry professionals aiming to enhance the robustness and reliability of their DL models.

Learning objectives

This tutorial will enable participants to gain knowledge about topics ranging from the very fundamentals of uncertainty quantification to cutting-edge recent methods in the field. We expect tutorial attendees to develop:

• A solid understanding of the basic concepts around uncertainty quantification in machine learning and of a taxonomical map of the field.

• The ability to discriminate between different sources of uncertainty.

• Mathematical fundamentals for understanding and implementing several well-established uncertainty quantification techniques

• Understanding of the UQ methods used for the medical image analysis tasks

• Knowledge of the tasks of robustness to and detection of Out-of-Distribution data and Model Calibration.

• Knowledge of uncertainty quantification through the basics of Conformal Prediction.

Preliminary program

Part 1: General Introduction into Uncertainty & Domain Shift

by Vatsal Raina

Part 2: Uncertainty for Medical Image Analysis

by Nataliia Molchanova

Part 3: Lightning Session: Model Calibration

by Meritxell Riera i Marín

Part 4: Lightning Session: Conformal Prediction

by Adrian Galdran

Minimal required skills

This tutorial does not require any prior skills in uncertainty or model calibration, however some background in deep learning may be needed.

Materials

All the materials of the previous year (UQ Tutorial MICCAI 2023) are currently available at our GitHub. Materials will be updated during summer with new presentations, videos, and hands-ons.