54 citations · 88 across the 11 of their papers we have counts for
7 papers · 1 filter
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…
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…
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…