640 citations · 3.8k across the 145 of their papers we have counts for
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Learning to restore images degraded by atmospheric turbulence using uncertainty
Rajeev Yasarla, Vishal M. Patel
Atmospheric turbulence can significantly degrade the quality of images acquired by long-range imaging systems by causing spatially and temporally random fluctuations in the index o…
Simultaneous Bone and Shadow Segmentation Network using Task Correspondence Consistency
Aimon Rahman, Jeya Maria Jose Valanarasu, Ilker Hacihaliloglu +1
Segmenting both bone surface and the corresponding acoustic shadow are fundamental tasks in ultrasound (US) guided orthopedic procedures. However, these tasks are challenging due t…
Orientation-guided Graph Convolutional Network for Bone Surface Segmentation
Aimon Rahman, Wele Gedara Chaminda Bandara, Jeya Maria Jose Valanarasu +2
Due to imaging artifacts and low signal-to-noise ratio in ultrasound images, automatic bone surface segmentation networks often produce fragmented predictions that can hinder the s…
SAR Despeckling using a Denoising Diffusion Probabilistic Model
Malsha V. Perera, Nithin Gopalakrishnan Nair, Wele Gedara Chaminda Bandara +1
Speckle is a multiplicative noise which affects all coherent imaging modalities including Synthetic Aperture Radar (SAR) images. The presence of speckle degrades the image quality…
Towards performant and reliable undersampled MR reconstruction via diffusion model sampling
Cheng Peng, Pengfei Guo, S. Kevin Zhou +2
Magnetic Resonance (MR) image reconstruction from under-sampled acquisition promises faster scanning time. To this end, current State-of-The-Art (SoTA) approaches leverage deep neu…
On-the-Fly Test-time Adaptation for Medical Image Segmentation
Jeya Maria Jose Valanarasu, Pengfei Guo, Vibashan VS +1
One major problem in deep learning-based solutions for medical imaging is the drop in performance when a model is tested on a data distribution different from the one that it is tr…
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…
Auto-FedRL: Federated Hyperparameter Optimization for Multi-institutional Medical Image Segmentation
Pengfei Guo, Dong Yang, Ali Hatamizadeh +14
Federated learning (FL) is a distributed machine learning technique that enables collaborative model training while avoiding explicit data sharing. The inherent privacy-preserving…
ReconFormer: Accelerated MRI Reconstruction Using Recurrent Transformer
Pengfei Guo, Yiqun Mei, Jinyuan Zhou +2
Accelerating magnetic resonance image (MRI) reconstruction process is a challenging ill-posed inverse problem due to the excessive under-sampling operation in k-space. In this pape…
Realistic Ultrasound Image Synthesis for Improved Classification of Liver Disease
Hui Che, Sumana Ramanathan, David Foran +3
With the success of deep learning-based methods applied in medical image analysis, convolutional neural networks (CNNs) have been investigated for classifying liver disease from ul…
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…
Multi-institutional Collaborations for Improving Deep Learning-based Magnetic Resonance Image Reconstruction Using Federated Learning
Pengfei Guo, Puyang Wang, Jinyuan Zhou +2
Fast and accurate reconstruction of magnetic resonance (MR) images from under-sampled data is important in many clinical applications. In recent years, deep learning-based methods…
Multi-Feature Multi-Scale CNN-Derived COVID-19 Classification from Lung Ultrasound Data
Hui Che, Jared Radbel, Jag Sunderram +3
The global pandemic of the novel coronavirus disease 2019 (COVID-19) has put tremendous pressure on the medical system. Imaging plays a complementary role in the management of pati…
Exploring Overcomplete Representations for Single Image Deraining using CNNs
Rajeev Yasarla, Jeya Maria Jose Valanarasu, Vishal M. Patel
Removal of rain streaks from a single image is an extremely challenging problem since the rainy images often contain rain streaks of different size, shape, direction and density. M…
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…
Confidence-guided Lesion Mask-based Simultaneous Synthesis of Anatomic and Molecular MR Images in Patients with Post-treatment Malignant Gliomas
Pengfei Guo, Puyang Wang, Rajeev Yasarla +3
Data-driven automatic approaches have demonstrated their great potential in resolving various clinical diagnostic dilemmas in neuro-oncology, especially with the help of standard a…
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…
Confidence Measure Guided Single Image De-raining
Rajeev Yasarla, Vishal M. Patel
Single image de-raining is an extremely challenging problem since the rainy images contain rain streaks which often vary in size, direction and density. This varying characteristic…