140 citations · 171 across the 9 of their papers we have counts for
9 papers
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…
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…
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…
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…
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…
Report of 2017 NSF Workshop on Multimedia Challenges, Opportunities and Research Roadmaps
Shih-Fu Chang, Alex Hauptmann, Louis-Philippe Morency +18
With the transformative technologies and the rapidly changing global R&D landscape, the multimedia and multimodal community is now faced with many new opportunities and uncertainti…
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…