18 citations · 23 across the 4 of their papers we have counts for
4 papers
Demystifying the Effect of Receptive Field Size in U-Net Models for Medical Image Segmentation
Vincent Loos, Rohit Pardasani, Navchetan Awasthi
Medical image segmentation is a critical task in healthcare applications, and U-Nets have demonstrated promising results. This work delves into the understudied aspect of receptive…
Multi-Threshold Attention U-Net (MTAU) based Model for Multimodal Brain Tumor Segmentation in MRI scans
Navchetan Awasthi, Rohit Pardasani, Swati Gupta
Gliomas are one of the most frequent brain tumors and are classified into high grade and low grade gliomas. The segmentation of various regions such as tumor core, enhancing tumor…
Sinogram super-resolution and denoising convolutional neural network (SRCN) for limited data photoacoustic tomography
Navchetan Awasthi, Rohit Pardasani, Sandeep Kumar Kalva +2
The quality of the reconstructed photoacoustic image largely depends on the amount of photoacoustic (PA) boundary data available, which in turn is proportional to the number of det…
Image Denoising and Super-Resolution using Residual Learning of Deep Convolutional Network
Rohit Pardasani, Utkarsh Shreemali
Image super-resolution and denoising are two important tasks in image processing that can lead to improvement in image quality. Image super-resolution is the task of mapping a low…