42 citations · 62 across the 13 of their papers we have counts for
13 papers
Self-supervised TransUNet for Ultrasound regional segmentation of the distal radius in children
Yuyue Zhou, Jessica Knight, Banafshe Felfeliyan +3
Supervised deep learning offers great promise to automate analysis of medical images from segmentation to diagnosis. However, their performance highly relies on the quality and qua…
Weakly Supervised Medical Image Segmentation With Soft Labels and Noise Robust Loss
Banafshe Felfeliyan, Abhilash Hareendranathan, Gregor Kuntze +4
Recent advances in deep learning algorithms have led to significant benefits for solving many medical image analysis problems. Training deep learning models commonly requires large…
Self-Supervised-RCNN for Medical Image Segmentation with Limited Data Annotation
Banafshe Felfeliyan, Abhilash Hareendranathan, Gregor Kuntze +4
Many successful methods developed for medical image analysis that are based on machine learning use supervised learning approaches, which often require large datasets annotated by…
Physics Driven Domain Specific Transporter Framework with Attention Mechanism for Ultrasound Imaging
Arpan Tripathi, Abhilash Rakkunedeth, Mahesh Raveendranatha Panicker +7
Most applications of deep learning techniques in medical imaging are supervised and require a large number of labeled data which is expensive and requires many hours of careful ann…
Unsupervised multi-latent space reinforcement learning framework for video summarization in ultrasound imaging
Roshan P Mathews, Mahesh Raveendranatha Panicker, Abhilash R Hareendranathan +6
The COVID-19 pandemic has highlighted the need for a tool to speed up triage in ultrasound scans and provide clinicians with fast access to relevant information. The proposed video…
Improved-Mask R-CNN: Towards an Accurate Generic MSK MRI instance segmentation platform (Data from the Osteoarthritis Initiative)
Banafshe Felfeliyan, Abhilash Hareendranathan, Gregor Kuntze +2
Objective assessment of Magnetic Resonance Imaging (MRI) scans of osteoarthritis (OA) can address the limitation of the current OA assessment. Segmentation of bone, cartilage, and…
Learning the Imaging Landmarks: Unsupervised Key point Detection in Lung Ultrasound Videos
Arpan Tripathi, Mahesh Raveendranatha Panicker, Abhilash R Hareendranathan +4
Lung ultrasound (LUS) is an increasingly popular diagnostic imaging modality for continuous and periodic monitoring of lung infection, given its advantages of non-invasiveness, non…
Domain Specific Transporter Framework to Detect Fractures in Ultrasound
Arpan Tripathi, Abhilash Rakkunedeth, Mahesh Raveendranatha Panicker +3
Ultrasound examination for detecting fractures is ideally suited for Emergency Departments (ED) as it is relatively fast, safe (from ionizing radiation), has dynamic imaging capabi…
Sample Efficient Learning of Image-Based Diagnostic Classifiers Using Probabilistic Labels
Roberto Vega, Pouneh Gorji, Zichen Zhang +5
Deep learning approaches often require huge datasets to achieve good generalization. This complicates its use in tasks like image-based medical diagnosis, where the small training…
A systematic review on the role of artificial intelligence in sonographic diagnosis of thyroid cancer: Past, present and future
Fatemeh Abdolali, Atefeh Shahroudnejad, Abhilash Rakkunedeth Hareendranathan +3
Thyroid cancer is common worldwide, with a rapid increase in prevalence across North America in recent years. While most patients present with palpable nodules through physical exa…
End-to-end detection-segmentation network with ROI convolution
Zichen Zhang, Min Tang, Dana Cobzas +3
We propose an end-to-end neural network that improves the segmentation accuracy of fully convolutional networks by incorporating a localization unit. This network performs object l…
Segmentation-by-Detection: A Cascade Network for Volumetric Medical Image Segmentation
Min Tang, Zichen Zhang, Dana Cobzas +2
We propose an attention mechanism for 3D medical image segmentation. The method, named segmentation-by-detection, is a cascade of a detection module followed by a segmentation modu…
Dependent Nonparametric Bayesian Group Dictionary Learning for online reconstruction of Dynamic MR images
Dornoosh Zonoobi, Shahrooz Faghih Roohi, Ashraf A. Kassim
In this paper, we introduce a dictionary learning based approach applied to the problem of real-time reconstruction of MR image sequences that are highly undersampled in k-space. U…