11 citations · 13 across the 10 of their papers we have counts for
17 papers
TICON: A Slide-Level Tile Contextualizer for Histopathology Representation Learning
Varun Belagali, Saarthak Kapse, Pierre Marza +12
The interpretation of small tiles in large whole slide images (WSI) often needs a larger image context. We introduce TICON, a transformer-based tile representation contextualizer t…
PEaRL: Pathway-Enhanced Representation Learning for Gene and Pathway Expression Prediction from Histology
Sejuti Majumder, Saarthak Kapse, Moinak Bhattacharya +3
Integrating histopathology with spatial transcriptomics (ST) provides a powerful opportunity to link tissue morphology with molecular function. Yet most existing multimodal approac…
SoC-DT: Standard-of-Care Aligned Digital Twins for Patient-Specific Tumor Dynamics
Moinak Bhattacharya, Gagandeep Singh, Prateek Prasanna
Accurate prediction of tumor trajectories under standard-of-care (SoC) therapies remains a major unmet need in oncology. This capability is essential for optimizing treatment plann…
Anatomy-DT: A Cross-Diffusion Digital Twin for Anatomical Evolution
Moinak Bhattacharya, Gagandeep Singh, Prateek Prasanna
Accurately modeling the spatiotemporal evolution of tumor morphology from baseline imaging is a pre-requisite for developing digital twin frameworks that can simulate disease progr…
NeuroRAD-FM: A Foundation Model for Neuro-Oncology with Distributionally Robust Training
Moinak Bhattacharya, Angelica P. Kurtz, Fabio M. Iwamoto +2
Neuro-oncology poses unique challenges for machine learning due to heterogeneous data and tumor complexity, limiting the ability of foundation models (FMs) to generalize across coh…
PixCell: A generative foundation model for digital histopathology images
Srikar Yellapragada, Alexandros Graikos, Zilinghan Li +11
The digitization of histology slides has revolutionized pathology, providing massive datasets for cancer diagnosis and research. Self-supervised and vision-language models have bee…