4 citations · 9 across the 7 of their papers we have counts for
8 papers
A Classical-Quantum Convolutional Neural Network for Detecting Pneumonia from Chest Radiographs
Viraj Kulkarni, Sanjesh Pawale, Amit Kharat
While many quantum computing techniques for machine learning have been proposed, their performance on real-world datasets remains to be studied. In this paper, we explore how a var…
Reducing Labelled Data Requirement for Pneumonia Segmentation using Image Augmentations
Jitesh Seth, Rohit Lokwani, Viraj Kulkarni +2
Deep learning semantic segmentation algorithms can localise abnormalities or opacities from chest radiographs. However, the task of collecting and annotating training data is expen…
Key Technology Considerations in Developing and Deploying Machine Learning Models in Clinical Radiology Practice
Viraj Kulkarni, Manish Gawali, Amit Kharat
The use of machine learning to develop intelligent software tools for interpretation of radiology images has gained widespread attention in recent years. The development, deploymen…
Comparative Evaluation of 3D and 2D Deep Learning Techniques for Semantic Segmentation in CT Scans
Abhishek Shivdeo, Rohit Lokwani, Viraj Kulkarni +2
Image segmentation plays a pivotal role in several medical-imaging applications by assisting the segmentation of the regions of interest. Deep learning-based approaches have been w…
Deep Learning Models for Calculation of Cardiothoracic Ratio from Chest Radiographs for Assisted Diagnosis of Cardiomegaly
Tanveer Gupte, Mrunmai Niljikar, Manish Gawali +3
We propose an automated method based on deep learning to compute the cardiothoracic ratio and detect the presence of cardiomegaly from chest radiographs. We develop two separate mo…
Automated Detection of COVID-19 from CT Scans Using Convolutional Neural Networks
Rohit Lokwani, Ashrika Gaikwad, Viraj Kulkarni +2
COVID-19 is an infectious disease that causes respiratory problems similar to those caused by SARS-CoV (2003). Currently, swab samples are being used for its diagnosis. The most co…