2 citations · 7 across the 21 of their papers we have counts for
6 papers · 1 filter
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…
Estimation and Analysis of Slice Propagation Uncertainty in 3D Anatomy Segmentation
Rachaell Nihalaani, Tushar Kataria, Jadie Adams +1
Supervised methods for 3D anatomy segmentation demonstrate superior performance but are often limited by the availability of annotated data. This limitation has led to a growing in…
StainDiffuser: MultiTask Dual Diffusion Model for Virtual Staining
Tushar Kataria, Beatrice Knudsen, Shireen Y. Elhabian
Hematoxylin and Eosin (H&E) staining is widely regarded as the standard in pathology for diagnosing diseases and tracking tumor recurrence. While H&E staining shows tissue structur…
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…