3 citations · 7 across the 10 of their papers we have counts for
4 papers · 1 filter
Anytime, Anywhere, Anyone: Investigating the Feasibility of Segment Anything Model for Crowd-Sourcing Medical Image Annotations
Pranav Kulkarni, Adway Kanhere, Dharmam Savani +4
Curating annotations for medical image segmentation is a labor-intensive and time-consuming task that requires domain expertise, resulting in "narrowly" focused deep learning (DL)…
Towards Resource-Efficient Streaming of Large-Scale Medical Image Datasets for Deep Learning
Pranav Kulkarni, Adway Kanhere, Eliot Siegel +2
Large-scale medical imaging datasets have accelerated deep learning (DL) for medical image analysis. However, the large scale of these datasets poses a challenge for researchers, r…
SegViz: A federated-learning based framework for multi-organ segmentation on heterogeneous data sets with partial annotations
Adway U. Kanhere, Pranav Kulkarni, Paul H. Yi +1
Segmentation is one of the most primary tasks in deep learning for medical imaging, owing to its multiple downstream clinical applications. However, generating manual annotations f…
An Interpretable Generative Model for Handwritten Digit Image Synthesis
Yao Zhu, Saksham Suri, Pranav Kulkarni +3
An interpretable generative model for handwritten digits synthesis is proposed in this work. Modern image generative models, such as Generative Adversarial Networks (GANs) and Vari…