3 citations · 8 across the 9 of their papers we have counts for
5 papers · 1 filter
Does image resolution impact chest X-ray based fine-grained Tuberculosis-consistent lesion segmentation?
Sivaramakrishnan Rajaraman, Feng Yang, Ghada Zamzmi +2
Deep learning (DL) models are state-of-the-art in segmenting anatomical and disease regions of interest (ROIs) in medical images. Particularly, a large number of DL-based technique…
Generalizability of Deep Adult Lung Segmentation Models to the Pediatric Population: A Retrospective Study
Sivaramakrishnan Rajaraman, Feng Yang, Ghada Zamzmi +2
Lung segmentation in chest X-rays (CXRs) is an important prerequisite for improving the specificity of diagnoses of cardiopulmonary diseases in a clinical decision support system.…
Deep ensemble learning for segmenting tuberculosis-consistent manifestations in chest radiographs
Sivaramakrishnan Rajaraman, Feng Yang, Ghada Zamzmi +3
Automated segmentation of tuberculosis (TB)-consistent lesions in chest X-rays (CXRs) using deep learning (DL) methods can help reduce radiologist effort, supplement clinical decis…
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…