4 papers
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…
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…
Digital Volumetric Biopsy Cores Improve Gleason Grading of Prostate Cancer Using Deep Learning
Ekaterina Redekop, Mara Pleasure, Zichen Wang +5
Prostate cancer (PCa) was the most frequently diagnosed cancer among American men in 2023. The histological grading of biopsies is essential for diagnosis, and various deep learnin…