5 papers
Pretext Matters: An Empirical Study of SSL Methods in Medical Imaging
Vedrana IveziÄ, Mara Pleasure, Ashwath Radhachandran +7
Though self-supervised learning (SSL) has demonstrated incredible ability to learn robust representations from unlabeled data, the choice of optimal SSL strategy can lead to vastly…
US-JEPA: A Joint Embedding Predictive Architecture for Medical Ultrasound
Ashwath Radhachandran, Vedrana IveziÄ, Shreeram Athreya +3
Ultrasound (US) imaging poses unique challenges for representation learning due to its inherently noisy acquisition process. The low signal-to-noise ratio and stochastic speckle pa…
Computational Mapping of Reactive Stroma in Prostate Cancer Yields Interpretable, Prognostic Biomarkers
Mara Pleasure, Ekaterina Redekop, Dhakshina Ilango +9
Current histopathological grading of prostate cancer relies primarily on glandular architecture, largely overlooking the tumor microenvironment. Here, we present PROTAS, a deep lea…
SPADE: Spatial Transcriptomics and Pathology Alignment Using a Mixture of Data Experts for an Expressive Latent Space
Ekaterina Redekop, Mara Pleasure, Zichen Wang +4
The rapid growth of digital pathology and advances in self-supervised deep learning have enabled the development of foundational models for various pathology tasks across diverse d…
Zero-shot Medical Event Prediction Using a Generative Pre-trained Transformer on Electronic Health Records
Ekaterina Redekop, Zichen Wang, Rushikesh Kulkarni +7
Longitudinal data in electronic health records (EHRs) represent an individual`s clinical history through a sequence of codified concepts, including diagnoses, procedures, medicatio…