13 citations · 18 across the 13 of their papers we have counts for
7 papers · 1 filter
Reinforcement Learning for Ultrasound Image Analysis A Comprehensive Review of Advances and Applications
Maha Ezzelarab, Midhila Madhusoodanan, Shrimanti Ghosh +3
Over the last decade, the use of machine learning (ML) approaches in medicinal applications has increased manifold. Most of these approaches are based on deep learning, which aims…
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…
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…
A New Semi-Automated Algorithm for Volumetric Segmentation of the Left Ventricle in Temporal 3D Echocardiography Sequences
Deepa Krishnaswamy, Abhilash R. Hareendranathan, Tan Suwatanaviroj +4
Purpose: Echocardiography is commonly used as a non-invasive imaging tool in clinical practice for the assessment of cardiac function. However, delineation of the left ventricle is…
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…