Publications

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Journal Articles

Graph attention-based fusion of pathology images and gene expression for prediction of cancer survival

Published in IEEE Transactions on Medical Imaging, 2024

We present an attention-based fusion architecture that integrates a graph representation of pathology images with gene expression data and concomitantly learns from the fused information to predict patient-specific survival.

Recommended citation: Y. Zheng, R. D. Conrad, E. J. Green, E. J. Burks, M. Betke, J. E. Beane, V. B. Kolachalama, Graph attention-based fusion of pathology images and gene expression for prediction of cancer survival, IEEE Transactions on Medical Imaging 2024
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Graph perceiver network for lung tumor and bronchial premalignant lesion stratification from histopathology

Published in The American Journal of Pathology, 2024

We present a graph-based deep learning framework that leverages hematoxylin and eosin–stained pathology images to stratify bronchial premalignant lesions and predict their progression to invasive lung squamous cell carcinoma.

Recommended citation: R. Gindra, Y. Zheng, D. Venkatraman, R. Conrad, E. Green, S. Mazzilli, E. Billatos, M. Reid, E. Burks, V. B. Kolachalama, J. E. Beane, Graph perceiver network for lung tumor and bronchial premalignant lesion stratification from histopathology The American Journal of Pathology 2024.
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Computational Assessment of Early Diabetic Nephropathy

Published in Journal of the American Society of Nephrology, 2022

A deep learning framework known as a feature pyramid network (FPN) was implemented to classify digitized renal biopsies as class I or II DN. Our study identified several regions on the biopsy images as informative for prediction of class I vs II DN. Further analysis can elucidate the importance of various histopathological features of early stage DN.

Recommended citation: L. Claus, Y. Zhang, Y. Zheng, T. Surendan, V. Chitalia, P. Walker, C. Cassol, V. B. Kolachalama, Computational Assessment of Early Diabetic Nephropathy, Journal of the American Society of Nephrology 2022
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A deep learning-based graph-transformer for whole slide image classification

Published in IEEE Transactions on Medical Imaging, 2022

We present a Graph-Transformer (GT) based framework for processing pathology data, called GTP, that interprets morphological and spatial information at the WSI-level to predict disease grade.

Recommended citation: Y. Zheng, R. Gindra, M. Betke, J. E. Beane, V. B. Kolachalama, A deep learning-based graph-transformer for whole slide image classification, IEEE Transactions on Medical Imaging 2022
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Deep-learning–driven quantification of interstitial fibrosis in digitized kidney biopsies

Published in The American Journal of Pathology, 2021

Our framework to analyzing microscopic- and WSI-level changes in renal biopsies attempts to mimic the pathologist and provides a regional and contextual estimation of IFTA. Such methods can assist clinicopathologic diagnosis.

Recommended citation: Yi Zheng, Clarissa A. Cassol, Saemi Jung, Divya Veerapaneni, Vipul C. Chitalia, Kevin Ren, Shubha S. Bellur, Peter Boor, Laura M. Barisoni, Sushrut S. Waikar, Margrit Betke , Vijaya B. Kolachalama, Deep-learning–driven quantification of interstitial fibrosis in digitized kidney biopsies, The American Journal of Pathology 2021
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Conference Papers

Semantic-Based Sentence Recognition in Images Using Bimodal Deep Learning

Published in IEEE International Conference on Image Processing, 2021

Semantic-based Sentence Recognition (SSR) can efficiently understand the context between regions of text or between words in images by extracting sentences or paragraphs from images instead of only isolated text regions or words.

Recommended citation: Yi Zheng, Qitong Wang, Margrit Betke, Semantic-Based Sentence Recognition in Images Using Bimodal Deep Learning, IEEE International Conference on Image Processing
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LAL: Linguistically aware learning for scene text recognition

Published in ACM International Conference on Multimedia, 2020

Linguistically Aware Learning (LAL) scene text recognizer is a a bimodal framework that simultaneously utilizes visual and linguistic information to enhance scene text recognition performance.

Recommended citation: Y. Zheng, W. Qin, D. Wijaya, and M. Betke, LAL: Linguistically aware learning for scene text recognition, ACM International Conference on Multimedia 2022
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A method for detecting text of arbitrary shapes in natural scenes that improves text spotting

Published in Computer Vision and Pattern Recognition Workshop, 2019

UHT, short for UNet, Heatmap, and Textfill, uses a UNet to compute heatmaps for candidate text regions and a textfill algorithm to produce tight polygonal boundaries around each word in the candidate text.

Recommended citation: Q. Wang, Y. Zheng, and M. Betke, A method for detecting text of arbitrary shapes in natural scenes that improves text spotting, Computer Vision and Pattern Recognition Workshop 2020
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Scraping Social Media Photos Posted in Kenya and Elsewhere to Detect and Analyze Food Types

Published in International Workshop on Multimedia Assisted Dietary Management, 2019

We propose a scrape-by-location methodology to create food image datasets from Instagram posts. We applied our techniques to the millions of Instagram posts we collected across Kenya over a period of 20 days to give an example of the kind of analysis social scientists may conduct with our tools.

Recommended citation: M. Jalal, K. Wang, S. Jefferson, Y. Zheng, E. O. Nsoesie, M. Betke, 'Scraping Social Media Photos Posted in Kenya and Elsewhere to Detect and Analyze Food Types', Proceedings of the 5th International Workshop on Multimedia Assisted Dietary Management, 2019
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