10 citations · 22 across the 6 of their papers we have counts for
6 papers
Few-Shot Concept Prompt Learning for Segmentation Foundation Models via Visual Grounding
Rahul Venkataramani, Rachana Sathish
Promptable segmentation foundation models (FMs) such as SAM3 and Medical SAM3 promise few-shot, interactively-specified segmentation for medical imaging through a natural language…
Synthetic Simplicity: Unveiling Bias in Medical Data Augmentation
Krishan Agyakari Raja Babu, Rachana Sathish, Mrunal Pattanaik +1
Synthetic data is becoming increasingly integral in data-scarce fields such as medical imaging, serving as a substitute for real data. However, its inherent statistical characteris…
Task-driven Prompt Evolution for Foundation Models
Rachana Sathish, Rahul Venkataramani, K S Shriram +1
Promptable foundation models, particularly Segment Anything Model (SAM), have emerged as a promising alternative to the traditional task-specific supervised learning for image segm…
Towards Continuous Domain adaptation for Healthcare
Rahul Venkataramani, Hariharan Ravishankar, Saihareesh Anamandra
Deep learning algorithms have demonstrated tremendous success on challenging medical imaging problems. However, post-deployment, these algorithms are susceptible to data distributi…
Understanding the Mechanisms of Deep Transfer Learning for Medical Images
Hariharan Ravishankar, Prasad Sudhakar, Rahul Venkataramani +4
The ability to automatically learn task specific feature representations has led to a huge success of deep learning methods. When large training data is scarce, such as in medical…
Filter sharing: Efficient learning of parameters for volumetric convolutions
Rahul Venkataramani, Sheshadri Thiruvenkadam, Prasad Sudhakar +2
Typical convolutional neural networks (CNNs) have several millions of parameters and require a large amount of annotated data to train them. In medical applications where training…