88 citations · 117 across the 15 of their papers we have counts for
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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…
Trilateral Attention Network for Real-time Medical Image Segmentation
Ghada Zamzmi, Vandana Sachdev, Sameer Antani
Accurate segmentation of medical images into anatomically meaningful regions is critical for the extraction of quantitative indices or biomarkers. The common pipeline for segmentat…
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…