3 citations · 3 across the 3 of their papers we have counts for
3 papers
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 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…