88 citations · 115 across the 14 of their papers we have counts for
14 papers
Multi-task Cross-modal Learning for Chest X-ray Image Retrieval
Zhaohui Liang, Sivaramakrishnan Rajaraman, Niccolo Marini +2
CLIP and BiomedCLIP are examples of vision-language foundation models and offer strong cross-modal embeddings; however, they are not optimized for fine-grained medical retrieval ta…
Evaluating Strategies for Synthesizing Clinical Notes for Medical Multimodal AI
Niccolo Marini, Zhaohui Liang, Sivaramakrishnan Rajaraman +2
Multimodal (MM) learning is emerging as a promising paradigm in biomedical artificial intelligence (AI) applications, integrating complementary modality, which highlight different…
Uncovering the effects of model initialization on deep model generalization: A study with adult and pediatric Chest X-ray images
Sivaramakrishnan Rajaraman, Ghada Zamzmi, Feng Yang +3
Model initialization techniques are vital for improving the performance and reliability of deep learning models in medical computer vision applications. While much literature exist…
Semantically Redundant Training Data Removal and Deep Model Classification Performance: A Study with Chest X-rays
Sivaramakrishnan Rajaraman, Ghada Zamzmi, Feng Yang +3
Deep learning (DL) has demonstrated its innate capacity to independently learn hierarchical features from complex and multi-dimensional data. A common understanding is that its per…
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…
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…
Unified Representation Learning for Efficient Medical Image Analysis
Ghada Zamzmi, Sivaramakrishnan Rajaraman, Sameer Antani
Medical image analysis typically includes several tasks such as enhancement, segmentation, and classification. Traditionally, these tasks are implemented using separate deep learni…
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…