2 citations · 7 across the 21 of their papers we have counts for
15 papers · 1 filter
FLASHSWIN: Unlocking Large Windows and Dense Tokens in Swin Vision Transformers with Memory Efficient Attention
Tushar Kataria, Gerald Sabin, Ponnuswamy Sadayappan +1
High-resolution vision backbones have long been forced to trade away local token density to afford larger receptive fields. Hierarchical Swin transformers impose this compromise be…
Inference-Time Orthogonal Seeding Enables Geometry-Aligned 3D Organ Segmentation for Slice-Propagation Methods
Md Rakibul Haque, Tushar Kataria, Shireen Y. Elhabian
Dense voxel-level annotation remains a major bottleneck in 3D medical image segmentation. Single-slice propagation methods such as Sli2Vol reduce this burden by propagating one ann…
MedConcept: Unsupervised Concept Discovery for Interpretability in Medical VLMs
Md Rakibul Haque, KM Arefeen Sultan, Tushar Kataria +1
While medical Vision-Language models (VLMs) achieve strong performance on tasks such as tumor or organ segmentation and diagnosis prediction, their opaque latent representations li…
MorphoFlow: Sparse-Supervised Generative Shape Modeling with Adaptive Latent Relevance
Mokshagna Sai Teja Karanam, Tushar Kataria, Shireen Elhabian
Statistical shape modeling (SSM) is central to population level analysis of anatomical variability, yet most existing approaches rely on densely annotated segmentations and fixed l…
MapVerse: A Benchmark for Geospatial Question Answering on Diverse Real-World Maps
Sharat Bhat, Harshita Khandelwal, Tushar Kataria +1
Maps are powerful carriers of structured and contextual knowledge, encompassing geography, demographics, infrastructure, and environmental patterns. Reasoning over such knowledge r…
Building Trust in Virtual Immunohistochemistry: Automated Assessment of Image Quality
Tushar Kataria, Shikha Dubey, Mary Bronner +4
Deep learning models can generate virtual immunohistochemistry (IHC) stains from hematoxylin and eosin (H&E) images, offering a scalable and low-cost alternative to laboratory IHC.…
BoundarySeg:Boundary-Aware Multi-Task Learning for Semi-Supervised 3D Medical Image Segmentation with Limited Unlabeled Data
Tushar Kataria, Shireen Y. Elhabian
Data scarcity is a primary bottleneck in 3D medical image segmentation: expert voxel-level annotations are expensive, and privacy regulations often severely restrict access to unan…
On the Viability of Semi-Supervised Segmentation Methods for Statistical Shape Modeling
Asma Khan, Tushar Kataria, Janmesh Ukey +1
Statistical Shape Models (SSMs) excel at identifying population level anatomical variations, which is at the core of various clinical and biomedical applications, including morphol…
NTSEBENCH: Cognitive Reasoning Benchmark for Vision Language Models
Pranshu Pandya, Vatsal Gupta, Agney S Talwarr +3
Cognitive textual and visual reasoning tasks, including puzzles, series, and analogies, demand the ability to quickly reason, decipher, and evaluate patterns both textually and spa…
EfficientMorph: Parameter-Efficient Transformer-Based Architecture for 3D Image Registration
Abu Zahid Bin Aziz, Mokshagna Sai Teja Karanam, Tushar Kataria +1
Transformers have emerged as the state-of-the-art architecture in medical image registration, outperforming convolutional neural networks (CNNs) by addressing their limited recepti…
MASSM: An End-to-End Deep Learning Framework for Multi-Anatomy Statistical Shape Modeling Directly From Images
Janmesh Ukey, Tushar Kataria, Shireen Y. Elhabian
Statistical Shape Modeling (SSM) effectively analyzes anatomical variations within populations but is limited by the need for manual localization and segmentation, which relies on…
Structural Cycle GAN for Virtual Immunohistochemistry Staining of Gland Markers in the Colon
Shikha Dubey, Tushar Kataria, Beatrice Knudsen +1
With the advent of digital scanners and deep learning, diagnostic operations may move from a microscope to a desktop. Hematoxylin and Eosin (H&E) staining is one of the most freque…
To pretrain or not to pretrain? A case study of domain-specific pretraining for semantic segmentation in histopathology
Tushar Kataria, Beatrice Knudsen, Shireen Elhabian
Annotating medical imaging datasets is costly, so fine-tuning (or transfer learning) is the most effective method for digital pathology vision applications such as disease classifi…
ADASSM: Adversarial Data Augmentation in Statistical Shape Models From Images
Mokshagna Sai Teja Karanam, Tushar Kataria, Krithika Iyer +1
Statistical shape models (SSM) have been well-established as an excellent tool for identifying variations in the morphology of anatomy across the underlying population. Shape model…
Unsupervised Domain Adaptation for Medical Image Segmentation via Feature-space Density Matching
Tushar Kataria, Beatrice Knudsen, Shireen Elhabian
Semantic segmentation is a critical step in automated image interpretation and analysis where pixels are classified into one or more predefined semantically meaningful classes. Dee…