MICCAI Society

📢 Women in MICCAI (WiM) is happy to announce the second invited talk in the WiM "Health Equity in the AI Era" Webinar Series 2024 🌟 Building Trustworthiness through Robustness 🙎♀️ Speaker: Dr. Maria A. Zuluaga, EURECOM, King's College London 📅 Monday, December 9, 2024 ⏲️ 4:00 - 5:00 PM UTC / 8:00 - 9:00 AM PST / 11:00 AM – 12:00 PM EST 🔗 Registration link: https://lnkd.in/gHJUQ6Cz 🗞️ Trustworthiness is paramount in the development and deployment of AI systems. A critical aspect of achieving trustworthy AI lies in its robustness—the ability of a model to maintain performance under varying conditions. This talk will delve into the importance of robustness in ensuring trustworthy AI, with a particular focus on two key areas: distribution shifts and input data variations. Distribution shifts, a common and well-studied challenge in AI, occur when the distribution of test data drifts from that of training. We will discuss techniques to enhance model robustness against such shifts, when dealing with multi-center, multi-modal and multi-organ datasets. Additionally, we will explore the less-studied area of input data variations, such as missing values or modalities. While the standard approach is to impute the missing data, we argue this may be problematic in certain scenarios that we will discuss. Then, we will present our research on developing robust models that can effectively handle missing data without imputation, leading to more reliable, resilient and, overall, more trustworthy AI systems. We will illustrate our work in different medical imaging applications.

📢 MICCAI Society MEMBERS are invited to vote in this year's #MICCAI Society Board election. The election will be held on the MICCAI WildApricot system in order to authenticate members in good standing. Members were sent an email communication on November 25. If you are a member who did not receive this notification, please check all your inbox folders, including your spam folder. Please ensure that emails from miccai@wildapricot.org are not being blocked by your mail server. 📅 Election closes on December 16, 2024 More information is also on our website: 🔗 https://lnkd.in/gA5M8vK9

📢 Join the next MICCAI Industrial Talk: Open-source Foundation Models for 3D Medical Image Segmentation and Generation Speakers: Dr. Yufan He and Dr. Can Zhao, NVIDIA 🗓️ Monday, November 25, 2024 ⏰ Time: 10:30-11:30 am EST / 4:30 - 5:30 pm CET Registration (required) is free and open to all: 🔗 https://lnkd.in/gzmAt6cG

📢 Women in MICCAI (WiM) is happy to announce the second invited talk in the WiM "Health Equity in the AI Era" Webinar Series 2024 🌟 Building Trustworthiness through Robustness 🙎♀️ Speaker: Dr. Maria A. Zuluaga, EURECOM, King's College London 📅 Monday, December 9, 2024 ⏲️ 4:00 - 5:00 PM UTC / 8:00 - 9:00 AM PST / 11:00 AM – 12:00 PM EST 🔗 Registration link: https://lnkd.in/gHJUQ6Cz 🗞️ Trustworthiness is paramount in the development and deployment of AI systems. A critical aspect of achieving trustworthy AI lies in its robustness—the ability of a model to maintain performance under varying conditions. This talk will delve into the importance of robustness in ensuring trustworthy AI, with a particular focus on two key areas: distribution shifts and input data variations. Distribution shifts, a common and well-studied challenge in AI, occur when the distribution of test data drifts from that of training. We will discuss techniques to enhance model robustness against such shifts, when dealing with multi-center, multi-modal and multi-organ datasets. Additionally, we will explore the less-studied area of input data variations, such as missing values or modalities. While the standard approach is to impute the missing data, we argue this may be problematic in certain scenarios that we will discuss. Then, we will present our research on developing robust models that can effectively handle missing data without imputation, leading to more reliable, resilient and, overall, more trustworthy AI systems. We will illustrate our work in different medical imaging applications.