Meeting Banner
Abstract #4006

Self-supervised Learning Network on Large Prostate Cancer mpMRI Dataset: Towards A Foundational Model of the Prostate

Noah Lowry1, Adrian Lazaro Breto1, Veronica Wallaengen1, Ahmad Algohary1, and Radka Stoyanova1
1University of Miami, Miami, FL, United States

Synopsis

Keywords: Diagnosis/Prediction, AI/ML Software, Foundation Models, Unsupervised AI

Motivation: No foundation models currently exist for prostatic mpMRI analysis.

Goal(s): To develop a foundation model (U-Found) and to evaluate its embeddings for a series of downstream tasks.

Approach: Development of an encoder neural network that learns vector representations of prostate mpMRI through contrastive learning.

Results: U-Found embeddings successfully encode features of prostate MRI including presence of cancer without ever explicitly learning those labels under the self-supervised framework.

Impact: To the best of our knowledge, U-Found is the first foundation-like model developed for prostate mpMRI. The embeddings, combining cancer and overall prostate characteristics features can be used in comprehensive modeling of cancer progression or response to therapy.

How to access this content:

For one year after publication, abstracts and videos are only open to registrants of this annual meeting. Registrants should use their existing login information. Non-registrant access can be purchased via the ISMRM E-Library.

After one year, current ISMRM & ISMRT members get free access to both the abstracts and videos. Non-members and non-registrants must purchase access via the ISMRM E-Library.

After two years, the meeting proceedings (abstracts) are opened to the public and require no login information. Videos remain behind password for access by members, registrants and E-Library customers.

Click here for more information on becoming a member.

Keywords