From the 1 of 11 linked papers with an AI index.
11 papers
TRACE-PCa: Predicting Prostate Cancer Progression from Longitudinal MRI During Active Surveillance
Hongye Zeng, Shreeram Athreya, Dingyuan Dai +4
The paper introduces an end-to-end model that leverages sequential MRI scans and clinical data to predict pathological progression in prostate cancer patients under active surveill…
DSA-NRP: No-Reflow Prediction from Angiographic Perfusion Dynamics in Stroke EVT
Shreeram Athreya, Carlos Olivares, Ameera Ismail +3
Following successful large-vessel recanalization via endovascular thrombectomy (EVT) for acute ischemic stroke (AIS), some patients experience a complication known as no-reflow, de…
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…