11 citations · 15 across the 25 of their papers we have counts for
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Benchmarking Vision-Language Models for Automated Pathology Diagnosis and Report Generation
Yumi Lee, Harim Oh, Hyoryung Kim +52
The rapid advancement of vision-language models (VLMs) has accelerated progress in computational pathology; however, whole-slide image (WSI)-based pathology report generation remai…
Pathologist Attention-Aligned Report Generation for Prostate Histopathology
Ruoyu Xue, Suryakant Singh, Souradeep Chakraborty +12
The allocation of visual attention by pathologists during cancer diagnosis is a highly selective process that critically shapes the information extracted from whole-slide images (W…
Semantic Context-aware mOdality fUsion Transformer (SCOUT): A Context-Aware Multimodal Transformer for Concept-Grounded Pathology Report Generation
Suryakant Singh, Saarthak Kapse, Joel Saltz +1
Whole-slide pathology report generation requires models to integrate localized histomorphology, global tissue context, and diagnostically relevant semantic information, yet existin…
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…
Structure-Guided Histopathology Synthesis via Dual-LoRA Diffusion
Xuan Xu, Prateek Prasanna
Histopathology image synthesis plays an important role in tissue restoration, data augmentation, and modeling of tumor microenvironments. However, existing generative methods typic…
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…
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…
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…
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…
Gen-SIS: Generative Self-augmentation Improves Self-supervised Learning
Varun Belagali, Srikar Yellapragada, Alexandros Graikos +7
Self-supervised learning (SSL) methods have emerged as strong visual representation learners by training an image encoder to maximize similarity between features of different views…
ZoomLDM: Latent Diffusion Model for multi-scale image generation
Srikar Yellapragada, Alexandros Graikos, Kostas Triaridis +4
Diffusion models have revolutionized image generation, yet several challenges restrict their application to large-image domains, such as digital pathology and satellite imagery. Gi…
Hard Negative Sample Mining for Whole Slide Image Classification
Wentao Huang, Xiaoling Hu, Shahira Abousamra +2
Weakly supervised whole slide image (WSI) classification is challenging due to the lack of patch-level labels and high computational costs. State-of-the-art methods use self-superv…