15 citations · 18 across the 2 of their papers we have counts for
7 papers
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…
Chest X-ray Image Phase Features for Improved Diagnosis of COVID-19 Using Convolutional Neural Network
Xiao Qi, Lloyd Brown, David J. Foran +1
Recently, the outbreak of the novel Coronavirus disease 2019 (COVID-19) pandemic has seriously endangered human health and life. Due to limited availability of test kits, the need…
KiU-Net: Overcomplete Convolutional Architectures for Biomedical Image and Volumetric Segmentation
Jeya Maria Jose Valanarasu, Vishwanath A. Sindagi, Ilker Hacihaliloglu +1
Most methods for medical image segmentation use U-Net or its variants as they have been successful in most of the applications. After a detailed analysis of these "traditional" enc…
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…
Simultaneous Segmentation and Classification of Bone Surfaces from Ultrasound Using a Multi-feature Guided CNN
Puyang Wang, Vishal M. Patel, Ilker Hacihaliloglu
Various imaging artifacts, low signal-to-noise ratio, and bone surfaces appearing several millimeters in thickness have hindered the success of ultrasound (US) guided computer assi…