16 citations · 16 across the 6 of their papers we have counts for
6 papers
CRACKS: Crowdsourcing Resources for Analysis and Categorization of Key Subsurface faults
Mohit Prabhushankar, Kiran Kokilepersaud, Jorge Quesada +6
Crowdsourcing annotations has created a paradigm shift in the availability of labeled data for machine learning. Availability of large datasets has accelerated progress in common k…
Ophthalmic Biomarker Detection: Highlights from the IEEE Video and Image Processing Cup 2023 Student Competition
Ghassan AlRegib, Mohit Prabhushankar, Kiran Kokilepersaud +5
The VIP Cup offers a unique experience to undergraduates, allowing students to work together to solve challenging, real-world problems with video and image processing techniques. I…
FOCAL: A Cost-Aware Video Dataset for Active Learning
Kiran Kokilepersaud, Yash-Yee Logan, Ryan Benkert +6
In this paper, we introduce the FOCAL (Ford-OLIVES Collaboration on Active Learning) dataset which enables the study of the impact of annotation-cost within a video active learning…
Clinical Trial Active Learning
Zoe Fowler, Kiran Kokilepersaud, Mohit Prabhushankar +1
This paper presents a novel approach to active learning that takes into account the non-independent and identically distributed (non-i.i.d.) structure of a clinical trial setting.…
Clinically Labeled Contrastive Learning for OCT Biomarker Classification
Kiran Kokilepersaud, Stephanie Trejo Corona, Mohit Prabhushankar +2
This paper presents a novel positive and negative set selection strategy for contrastive learning of medical images based on labels that can be extracted from clinical data. In the…
Exploiting the Distortion-Semantic Interaction in Fisheye Data
Kiran Kokilepersaud, Mohit Prabhushankar, Yavuz Yarici +2
In this work, we present a methodology to shape a fisheye-specific representation space that reflects the interaction between distortion and semantic context present in this data m…