Tassilo Wald - An OpenMind for 3D medical vision self supervised learning

Self-supervised learning in 3D medical imaging has been hindered by three key challenges: limited pre-training dataset sizes, suboptimal architectures for 3D segmentation, and inconsistent evaluation practices. In this talk, I will present our recent efforts to bring clarity and consistency to the field by introducing a large public dataset and conducting a comprehensive benchmark study evaluating the current state of 3D medical self-supervised learning, along with key findings from this large-scale evaluation. Tassilo Wald is a Ph.D. student at the German Cancer Research Center (DKFZ) in the division of Medical Image Computing in Heidelberg. His research focuses on pre-training for 3D medical imaging analysis, which led him to work on 3D medical vision-language models at Microsoft Health Futures during an Internship and to lead the pre-training efforts of The Human Radiome Project, part of the Helmholtz Foundation Model Initiative. This session is brought to you by the Cohere Labs Open Science Community - a space where ML researchers, engineers, linguists, social scientists, and lifelong learners connect and collaborate with each other. We'd like to extend a special thank you to Leema Krishna, Anas Zafar and Oumayma Essarhi Leads of our ML Healthcare group for their dedication in organizing this event. If you’re interested in sharing your work, we welcome you to join us! Simply fill out the form at https://forms.gle/ALND9i6KouEEpCnz6 to express your interest in becoming a speaker. Join the Cohere Labs Open Science Community to see a full list of upcoming events (https://tinyurl.com/CohereLabsCommunityApp).