21 citations · 24 across the 4 of their papers we have counts for
4 papers
A bone suppression model ensemble to improve COVID-19 detection in chest X-rays
Sivaramakrishnan Rajaraman, Gregg Cohen, Lillian Spear +2
Chest X-ray (CXR) is a widely performed radiology examination that helps to detect abnormalities in the tissues and organs in the thoracic cavity. Detecting pulmonary abnormalities…
Chest X-Ray Bone Suppression for Improving Classification of Tuberculosis-Consistent Findings
Sivaramakrishnan Rajaraman, Ghada Zamzmi, Les Folio +2
Chest X-rays are the most commonly performed diagnostic examination to detect cardiopulmonary abnormalities. However, the presence of bony structures such as ribs and clavicles can…
Improved Semantic Segmentation of Tuberculosis-consistent findings in Chest X-rays Using Augmented Training of Modality-specific U-Net Models with Weak Localizations
Sivaramakrishnan Rajaraman, Les Folio, Jane Dimperio +2
Deep learning (DL) has drawn tremendous attention in object localization and recognition for both natural and medical images. U-Net segmentation models have demonstrated superior p…
Iteratively Pruned Deep Learning Ensembles for COVID-19 Detection in Chest X-rays
Sivaramakrishnan Rajaraman, Jen Siegelman, Philip O. Alderson +3
We demonstrate use of iteratively pruned deep learning model ensembles for detecting pulmonary manifestation of COVID-19 with chest X-rays. This disease is caused by the novel Seve…