3 citations · 6 across the 6 of their papers we have counts for
6 papers
Artery-Vein Segmentation from Fundus Images using Deep Learning
Sharan SK, Subin Sahayam, Umarani Jayaraman +1
Segmenting of clinically important retinal blood vessels into arteries and veins is a prerequisite for retinal vessel analysis. Such analysis can provide potential insights and bio…
Explainable AI: Comparative Analysis of Normal and Dilated ResNet Models for Fundus Disease Classification
P. N. Karthikayan, Yoga Sri Varshan, Hitesh Gupta Kattamuri +1
This paper presents dilated Residual Network (ResNet) models for disease classification from retinal fundus images. Dilated convolution filters are used to replace normal convoluti…
Detection of Under-represented Samples Using Dynamic Batch Training for Brain Tumor Segmentation from MR Images
Subin Sahayam, John Michael Sujay Zakkam, Yoga Sri Varshan +1
Brain tumors in magnetic resonance imaging (MR) are difficult, time-consuming, and prone to human error. These challenges can be resolved by developing automatic brain tumor segmen…
Integrating Edge Information into Ground Truth for the Segmentation of the Optic Disc and Cup from Fundus Images
Yoga Sri Varshan, Hitesh Gupta Kattamuri, Subin Sahayam +1
Optic disc and cup segmentation helps in the diagnosis of glaucoma, myocardial infarction, and diabetic retinopathy. Most deep learning methods developed to perform segmentation ta…
Integrating Edges into U-Net Models with Explainable Activation Maps for Brain Tumor Segmentation using MR Images
Subin Sahayam, Umarani Jayaraman
Manual delineation of tumor regions from magnetic resonance (MR) images is time-consuming, requires an expert, and is prone to human error. In recent years, deep learning models ha…
Can we learn better with hard samples?
Subin Sahayam, John Zakkam, Umarani Jayaraman
In deep learning, mini-batch training is commonly used to optimize network parameters. However, the traditional mini-batch method may not learn the under-represented samples and co…