88 citations · 115 across the 14 of their papers we have counts for
5 papers · 1 filter
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…
Does deep learning model calibration improve performance in class-imbalanced medical image classification?
Sivaramakrishnan Rajaraman, Prasanth Ganesan, Sameer Antani
In medical image classification tasks, it is common to find that the number of normal samples far exceeds the number of abnormal samples. In such class-imbalanced situations, relia…
Multi-loss ensemble deep learning for chest X-ray classification
Sivaramakrishnan Rajaraman, Ghada Zamzmi, Sameer Antani
Medical images commonly exhibit multiple abnormalities. Predicting them requires multi-class classifiers whose training and desired reliable performance can be affected by a combin…
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…