18 citations · 37 across the 15 of their papers we have counts for
17 papers
Gradient based Severity Labeling for Biomarker Classification in OCT
Kiran Kokilepersaud, Mohit Prabhushankar, Ghassan AlRegib +2
In this paper, we propose a novel selection strategy for contrastive learning for medical images. On natural images, contrastive learning uses augmentations to select positive and…
Countering Multi-modal Representation Collapse through Rank-targeted Fusion
Seulgi Kim, Kiran Kokilepersaud, Mohit Prabhushankar +1
Multi-modal fusion methods often suffer from two types of representation collapse: feature collapse where individual dimensions lose their discriminative power (as measured by eige…
Subject Invariant Contrastive Learning for Human Activity Recognition
Yavuz Yarici, Kiran Kokilepersaud, Mohit Prabhushankar +1
The high cost of annotating data makes self-supervised approaches, such as contrastive learning methods, appealing for Human Activity Recognition (HAR). Effective contrastive learn…
AdaDim: Dimensionality Adaptation for SSL Representational Dynamics
Kiran Kokilepersaud, Mohit Prabhushankar, Ghassan AlRegib
A key factor in effective Self-Supervised learning (SSL) is preventing dimensional collapse, where higher-dimensional representation spaces () span a lower-dimensional subspace.…
HEX: Hierarchical Emergence Exploitation in Self-Supervised Algorithms
Kiran Kokilepersaud, Seulgi Kim, Mohit Prabhushankar +1
In this paper, we propose an algorithm that can be used on top of a wide variety of self-supervised (SSL) approaches to take advantage of hierarchical structures that emerge during…
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…
Explaining Representation Learning with Perceptual Components
Yavuz Yarici, Kiran Kokilepersaud, Mohit Prabhushankar +1
Self-supervised models create representation spaces that lack clear semantic meaning. This interpretability problem of representations makes traditional explainability methods inef…
Taxes Are All You Need: Integration of Taxonomical Hierarchy Relationships into the Contrastive Loss
Kiran Kokilepersaud, Yavuz Yarici, Mohit Prabhushankar +1
In this work, we propose a novel supervised contrastive loss that enables the integration of taxonomic hierarchy information during the representation learning process. A supervise…
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…
Clinical Contrastive Learning for Biomarker Detection
Kiran Kokilepersaud, Mohit Prabhushankar, Ghassan AlRegib
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…
OLIVES Dataset: Ophthalmic Labels for Investigating Visual Eye Semantics
Mohit Prabhushankar, Kiran Kokilepersaud, Yash-yee Logan +3
Clinical diagnosis of the eye is performed over multifarious data modalities including scalar clinical labels, vectorized biomarkers, two-dimensional fundus images, and three-dimen…
Volumetric Supervised Contrastive Learning for Seismic Semantic Segmentation
Kiran Kokilepersaud, Mohit Prabhushankar, Ghassan AlRegib
In seismic interpretation, pixel-level labels of various rock structures can be time-consuming and expensive to obtain. As a result, there oftentimes exists a non-trivial quantity…
Multi-Modal Learning Using Physicians Diagnostics for Optical Coherence Tomography Classification
Y. Logan, K. Kokilepersaud, G. Kwon +3
In this paper, we propose a framework that incorporates experts diagnostics and insights into the analysis of Optical Coherence Tomography (OCT) using multi-modal learning. To demo…