1 citations · 1 across the 10 of their papers we have counts for
12 papers
GliomaINK: A Tumor and Anatomy Conditioned Flow Matching Framework for 3D MRI Sequence Synthesis in Glioma Patients
Moinak Bhattacharya, Annie Singh, Mohammad Chaudhry +2
Background: Incomplete multiparametric MR examinations limit glioma characterization and downstream AI tools. Generative models can synthesize missing MRI sequences, but anatomical…
Gaze2Report: Radiology Report Generation via Visual-Gaze Prompt Tuning of LLMs
Aishik Konwer, Moinak Bhattacharya, Prateek Prasanna
Existing deep learning methods for radiology report generation enhance diagnostic efficiency but often overlook physician-informed medical priors. This leads to a suboptimal alignm…
Vessel-Aware Deep Learning for OCTA-Based Detection of AMD
Margalit G. Mitzner, Moinak Bhattacharya, Zhilin Zou +2
Age-related macular degeneration (AMD) is characterized by early micro-vascular alterations that can be captured non-invasively using optical coherence tomography angiography (OCTA…
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…
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…
RadGazeGen: Radiomics and Gaze-guided Medical Image Generation using Diffusion Models
Moinak Bhattacharya, Gagandeep Singh, Shubham Jain +1
In this work, we present RadGazeGen, a novel framework for integrating experts' eye gaze patterns and radiomic feature maps as controls to text-to-image diffusion models for high f…
RadioTransformer: A Cascaded Global-Focal Transformer for Visual Attention-guided Disease Classification
Moinak Bhattacharya, Shubham Jain, Prateek Prasanna
In this work, we present RadioTransformer, a novel visual attention-driven transformer framework, that leverages radiologists' gaze patterns and models their visuo-cognitive behavi…