54 citations · 88 across the 11 of their papers we have counts for
12 papers
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…
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…
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…
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…
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…
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…
Brain Tumor Segmentation and Survival Prediction using 3D Attention UNet
Mobarakol Islam, Vibashan VS, V Jeya Maria Jose +3
In this work, we develop an attention convolutional neural network (CNN) to segment brain tumors from Magnetic Resonance Images (MRI). Further, we predict the survival rate using v…
Glioma Prognosis: Segmentation of the Tumor and Survival Prediction using Shape, Geometric and Clinical Information
Mobarakol Islam, V Jeya Maria Jose, Hongliang Ren
Segmentation of brain tumor from magnetic resonance imaging (MRI) is a vital process to improve diagnosis, treatment planning and to study the difference between subjects with tumo…
Medical Transformer: Gated Axial-Attention for Medical Image Segmentation
Jeya Maria Jose Valanarasu, Poojan Oza, Ilker Hacihaliloglu +1
Over the past decade, Deep Convolutional Neural Networks have been widely adopted for medical image segmentation and shown to achieve adequate performance. However, due to the inhe…
Overcomplete Deep Subspace Clustering Networks
Jeya Maria Jose Valanarasu, Vishal M. Patel
Deep Subspace Clustering Networks (DSC) provide an efficient solution to the problem of unsupervised subspace clustering by using an undercomplete deep auto-encoder with a fully-co…
KiU-Net: Towards Accurate Segmentation of Biomedical Images using Over-complete Representations
Jeya Maria Jose, Vishwanath Sindagi, Ilker Hacihaliloglu +1
Due to its excellent performance, U-Net is the most widely used backbone architecture for biomedical image segmentation in the recent years. However, in our studies, we observe tha…
Learning to Segment Brain Anatomy from 2D Ultrasound with Less Data
Jeya Maria Jose V., Rajeev Yasarla, Puyang Wang +2
Automatic segmentation of anatomical landmarks from ultrasound (US) plays an important role in the management of preterm neonates with a very low birth weight due to the increased…