140 citations · 179 across the 13 of their papers we have counts for
13 papers
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…
Deep Cervix Model Development from Heterogeneous and Partially Labeled Image Datasets
Anabik Pal, Zhiyun Xue, Sameer Antani
Cervical cancer is the fourth most common cancer in women worldwide. The availability of a robust automated cervical image classification system can augment the clinical care provi…
Selective Synthetic Augmentation with HistoGAN for Improved Histopathology Image Classification
Yuan Xue, Jiarong Ye, Qianying Zhou +7
Histopathological analysis is the present gold standard for precancerous lesion diagnosis. The goal of automated histopathological classification from digital images requires super…
Region Comparison Network for Interpretable Few-shot Image Classification
Zhiyu Xue, Lixin Duan, Wen Li +2
While deep learning has been successfully applied to many real-world computer vision tasks, training robust classifiers usually requires a large amount of well-labeled data. Howeve…
Synthetic Sample Selection via Reinforcement Learning
Jiarong Ye, Yuan Xue, L. Rodney Long +4
Synthesizing realistic medical images provides a feasible solution to the shortage of training data in deep learning based medical image recognition systems. However, the quality c…
Feature based Sequential Classifier with Attention Mechanism
Sudhir Sornapudi, R. Joe Stanley, William V. Stoecker +5
Cervical cancer is one of the deadliest cancers affecting women globally. Cervical intraepithelial neoplasia (CIN) assessment using histopathological examination of cervical biopsy…
Selective Synthetic Augmentation with Quality Assurance
Yuan Xue, Jiarong Ye, Rodney Long +3
Supervised training of an automated medical image analysis system often requires a large amount of expert annotations that are hard to collect. Moreover, the proportions of data av…
Comparing Deep Learning Models for Multi-cell Classification in Liquid-based Cervical Cytology Images
Sudhir Sornapudi, G. T. Brown, Zhiyun Xue +3
Liquid-based cytology (LBC) is a reliable automated technique for the screening of Papanicolaou (Pap) smear data. It is an effective technique for collecting a majority of the cerv…
Synthetic Augmentation and Feature-based Filtering for Improved Cervical Histopathology Image Classification
Yuan Xue, Qianying Zhou, Jiarong Ye +5
Cervical intraepithelial neoplasia (CIN) grade of histopathology images is a crucial indicator in cervical biopsy results. Accurate CIN grading of epithelium regions helps patholog…