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.
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