42 citations · 68 across the 16 of their papers we have counts for
6 papers · 1 filter
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…
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…