most citedIntegrating Edges into U-Net Models with Explainable Activation Maps for Brain Tumor Segmentation using MR Images

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eess.IV2024

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…

eess.IV2024★ 3 cited

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…