From the 1 of 15 linked papers with an AI index.
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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…
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…
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…
CytoFM: The first cytology foundation model
Vedrana IveziÄ, Ashwath Radhachandran, Ekaterina Redekop +5
Cytology is essential for cancer diagnostics and screening due to its minimally invasive nature. However, the development of robust deep learning models for digital cytology is cha…