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…
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…
Prototype-Guided Diffusion for Digital Pathology: Achieving Foundation Model Performance with Minimal Clinical Data
Ekaterina Redekop, Mara Pleasure, Vedrana Ivezic +5
Foundation models in digital pathology use massive datasets to learn useful compact feature representations of complex histology images. However, there is limited transparency into…