activity
20192022
most citedUNeXt: MLP-based Rapid Medical Image Segmentation Network

54 citations · 88 across the 11 of their papers we have counts for

collaborators

12 papers

eess.IV2022

Ischemic Stroke Lesion Segmentation Using Adversarial Learning

Mobarakol Islam, N Rajiv Vaidyanathan, V Jeya Maria Jose +1

Ischemic stroke occurs through a blockage of clogged blood vessels supplying blood to the brain. Segmentation of the stroke lesion is vital to improve diagnosis, outcome assessment…

eess.IV2022★ 54 cited

UNeXt: MLP-based Rapid Medical Image Segmentation Network

Jeya Maria Jose Valanarasu, Vishal M. Patel

UNet and its latest extensions like TransUNet have been the leading medical image segmentation methods in recent years. However, these networks cannot be effectively adopted for ra…

cs.CV2021★ 1 cited

TransWeather: Transformer-based Restoration of Images Degraded by Adverse Weather Conditions

Jeya Maria Jose Valanarasu, Rajeev Yasarla, Vishal M. Patel

Removing adverse weather conditions like rain, fog, and snow from images is an important problem in many applications. Most methods proposed in the literature have been designed to…

cs.CV2021

SPIN Road Mapper: Extracting Roads from Aerial Images via Spatial and Interaction Space Graph Reasoning for Autonomous Driving

Wele Gedara Chaminda Bandara, Jeya Maria Jose Valanarasu, Vishal M. Patel

Road extraction is an essential step in building autonomous navigation systems. Detecting road segments is challenging as they are of varying widths, bifurcated throughout the imag…

cs.CV2021★ 3 cited

Image Fusion Transformer

Vibashan VS, Jeya Maria Jose Valanarasu, Poojan Oza +1

In image fusion, images obtained from different sensors are fused to generate a single image with enhanced information. In recent years, state-of-the-art methods have adopted Convo…

eess.IV2021★ 4 cited

Over-and-Under Complete Convolutional RNN for MRI Reconstruction

Pengfei Guo, Jeya Maria Jose Valanarasu, Puyang Wang +3

Reconstructing magnetic resonance (MR) images from undersampled data is a challenging problem due to various artifacts introduced by the under-sampling operation. Recent deep learn…