2 citations · 5 across the 5 of their papers we have counts for
5 papers
Federated Black-Box Adaptation for Semantic Segmentation
Jay N. Paranjape, Shameema Sikder, S. Swaroop Vedula +1
Federated Learning (FL) is a form of distributed learning that allows multiple institutions or clients to collaboratively learn a global model to solve a task. This allows the mode…
S-SAM: SVD-based Fine-Tuning of Segment Anything Model for Medical Image Segmentation
Jay N. Paranjape, Shameema Sikder, S. Swaroop Vedula +1
Medical image segmentation has been traditionally approached by training or fine-tuning the entire model to cater to any new modality or dataset. However, this approach often requi…
AdaptiveSAM: Towards Efficient Tuning of SAM for Surgical Scene Segmentation
Jay N. Paranjape, Nithin Gopalakrishnan Nair, Shameema Sikder +2
Segmentation is a fundamental problem in surgical scene analysis using artificial intelligence. However, the inherent data scarcity in this domain makes it challenging to adapt tra…
Cross-Dataset Adaptation for Instrument Classification in Cataract Surgery Videos
Jay N. Paranjape, Shameema Sikder, Vishal M. Patel +1
Surgical tool presence detection is an important part of the intra-operative and post-operative analysis of a surgery. State-of-the-art models, which perform this task well on a pa…
GLSFormer: Gated - Long, Short Sequence Transformer for Step Recognition in Surgical Videos
Nisarg A. Shah, Shameema Sikder, S. Swaroop Vedula +1
Automated surgical step recognition is an important task that can significantly improve patient safety and decision-making during surgeries. Existing state-of-the-art methods for s…