11 citations · 15 across the 25 of their papers we have counts for
12 papers · 1 filter
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…
GazeLT: Visual attention-guided long-tailed disease classification in chest radiographs
Moinak Bhattacharya, Gagandeep Singh, Shubham Jain +1
In this work, we present GazeLT, a human visual attention integration-disintegration approach for long-tailed disease classification. A radiologist's eye gaze has distinct patterns…
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…
ImmunoDiff: A Diffusion Model for Immunotherapy Response Prediction in Lung Cancer
Moinak Bhattacharya, Judy Huang, Amna F. Sher +3
Accurately predicting immunotherapy response in Non-Small Cell Lung Cancer (NSCLC) remains a critical unmet need. Existing radiomics and deep learning-based predictive models rely…
BrainMRDiff: A Diffusion Model for Anatomically Consistent Brain MRI Synthesis
Moinak Bhattacharya, Saumya Gupta, Annie Singh +3
Accurate brain tumor diagnosis relies on the assessment of multiple Magnetic Resonance Imaging (MRI) sequences. However, in clinical practice, the acquisition of certain sequences…
GECKO: Gigapixel Vision-Concept Contrastive Pretraining in Histopathology
Saarthak Kapse, Pushpak Pati, Srikar Yellapragada +5
Pretraining a Multiple Instance Learning (MIL) aggregator enables the derivation of Whole Slide Image (WSI)-level embeddings from patch-level representations without supervision. W…
Pathology Image Compression with Pre-trained Autoencoders
Srikar Yellapragada, Alexandros Graikos, Kostas Triaridis +6
The growing volume of high-resolution Whole Slide Images in digital histopathology poses significant storage, transmission, and computational efficiency challenges. Standard compre…
Enhancing SAM with Efficient Prompting and Preference Optimization for Semi-supervised Medical Image Segmentation
Aishik Konwer, Zhijian Yang, Erhan Bas +4
Foundational models such as the Segment Anything Model (SAM) are gaining traction in medical imaging segmentation, supporting multiple downstream tasks. However, such models are su…