Expertise and Research Interests:

Dr. Guo received his PhD degree in Electrical Engineering from Missouri University of Science and Technology. He joined the Lister Hill National Center for Biomedical Communications (LHNCBC), National Library of Medicine (NLM), National Institutes of Health (NIH), as a postdoctoral researcher in 2018. His research interests are in the area of biomedical image analysis and computer vision, focusing on image classification, image segmentation and deep learning/machine learning based image information retrieval. He is being mentored by Sameer Antani, PhD., Staff Scientist, Communications Engineering Branch, NLM, NIH.

Publications:

Xue Z, Angara S, Guo P, Rajaraman S, Jeronimo J, Rodriguez AC, Alfaro K, Charoenkwan K, Mungo C, Fokom‐Domgue J, Wentzensen N, Desai K, Ajenifuja K, Wikström E, Befano BS, Antani S. Image Quality Classification for Automated Visual Evaluation of Cervical Precancer. Zamzmi, G., Antani, S., Bagci, U., Linguraru, M.G., Rajaraman, S., Xue, Z. (eds) Medical Image Learning with Limited and Noisy Data. MILLanD 2022. Lecture Notes in Computer Science, vol 13559. Springer, Cham. https://doi.org/10.1007/978-3-031-16760-7_20.

Rajaraman S, Guo P, Xue Z, Antani SK. A Deep Modality-Specific Ensemble for Improving Pneumonia Detection in Chest X-rays. Diagnostics (Basel). 2022 Jun 11;12(6). doi: 10.3390/diagnostics12061442. PubMed PMID: 35741252; PubMed Central PMCID: PMC9221627.

Angara S, Guo P, Xue Z, Antani S. An Empirical Study of Vision Transformers for Cervical Precancer Detection. In: Santosh, K., Hegadi, R., Pal, U. (eds) Recent Trends in Image Processing and Pattern Recognition. RTIP2R 2021. Communications in Computer and Information Science, vol 1576. Springer, Cham. https://doi.org/10.1007/978-3-031-07005-1_3.

Guo P, Xue Z, Angara S, Antani SK . Unsupervised Deep Learning Registration of Uterine Cervix Sequence Images. Cancers, 14(10), 2401. https://doi.org/10.3390/cancers14102401.

Xue Z, Guo P, Angara S, Pal A, Jeronimo J, Dasai KT, Ajenifuja KO, Adepiti CA, Schiffman M, Antani S. Cleaning Highly Unbalanced Multisource Image Dataset for Quality Control in Cervical Precancer Screening. Proceedings of the 4th International Conference on Recent Trends in Image Processing & Pattern Recognition (RTIP2R) 2021.

Angara S, Guo P, Xue Z, Antani S. Semi-Supervised Learning for Cervical Precancer Detection. 2021 IEEE 34th International Symposium on Computer-Based Medical Systems (CBMS), 2021, pp. 202-206, doi: 10.1109/CBMS52027.2021.00072.

Xue Z, Guo P, Desai K, Pal A, Ajenifuja KO, Adepiti CA, Long LR, Schiffman M, Antani S. A Deep Clustering Method for Analyzing Uterine Cervix Images Across Imaging Devices. 34th IEEE International Symposium on Computer-Based Medical Systems (CBMS), June 2021, pp. 527-532, doi: 10.1109/CBMS52027.2021.00085.

Guo P, Xue Z, Jeronimo J, Gage JC, Desai KT, Befano B, García F, Long LR, Schiffman M, Antani S. Network Visualization and Pyramidal Feature Comparison for Ablative Treatability Classification Using Digitized Cervix Images. J Clin Med. 2021 Mar 1;10(5):953. doi: 10.3390/jcm10050953.

Guo P, Xue Z, Mtema Z, Yeates K, Ginsburg O, Demarco M, Long LR, Schiffman M, Antani SK. 2. Ensemble Deep Learning for Cervix Image Selection toward Improving Reliability in Automated Cervical Precancer Screening. Diagnostics (Basel) 2020 jul3;10(7):451.

Xue Z, Novetsky A, Einstein M, Marcus J, Befano B, Guo P, Demarco M, Wenzenten N, Long LR, Schiffman M, Antani SK. A demonstration of automated visual evaluation of cervical images taken with a smartphone camera. Int J Cancer. 2020 Apr 30.

Guo P, Xue Z, Long LR, Antani SK. Anatomical landmark segmentation in uterine cervix images using deep learning. Proc. SPIE 11318, Medical Imaging 2020: Imaging Informatics for Healthcare, Research, and Applications, 1131810 (2 March 2020)

Guo P, Xue Z, Long LR, Antani SK. Cross-Dataset Evaluation of Deep Learning Networks for Uterine Cervix Segmentation. Diagnostics (Basel). 2020 Jan 14;10(1). pii: E44. doi: 10.3390/diagnostics10010044.

Guo P, Singh S, Xue Z, Long LR, Antani SK. Deep Learning for Assessing Image Focus for Automated Cervical Cancer Screening. 2019 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI) DOI: 10.1109/BHI.2019.8834495.

Almubarak H, Guo P, Stanley RJ, Long LR, Antani SK, Thoma GR. Algorithm Enhancements for Improvement of Localized Classification of Uterine Cervical Cancer Digital Histology Images. in Handbook of Research on Emerging Perspectives on Healthcare Information Systems and Informatics,. IGI Global (Hershey, PA).

Guo P, Stanley RJ, Cole JG, Hagerty JR, Stoecker WV. Color Feature-based Pillbox Image Color Recognition. Published 2017 in VISIGRAPP DOI:10.5220/0006136001880194.

Guo P, Almubarak H, Banerjee K, Stanley RJ, Long LR, Antani SK, Thoma GR, Zuna R, Frazier S, Moss R, Stoecker W. Enhancements in localized classification for uterine cervical cancer digital histology image assessment. J Pathol Inform. 2016 Dec 30;7:51. doi: 10.4103/2153-3539.197193. eCollection 2016.

Liang Z, Powell A, Ersoy I, Poostchi M, Silamut K, Palaniappan K, Guo P, Hossain M, Antani SK, Maude R, Huang J, Jaeger S, Thoma GR. CNN-Based Image Analysis for Malaria Diagnosis. IEEE International Conference on Bioinformatics & Biomedicine (BIBM), Shenzhen, China, 2016.

Guo P, Stanley RJ, De S, Long LR, Antani SK, Thoma GR, Demner-Fushman D, Sornapudia S. Features Advances to Automatically Find Images for Application to Clinical Decision Support. Medical Research Archives. 4(7) 2016. DOI: 10.18103/mra.v4i7.761.

Guo P, Banerjee K, Stanley RJ, Long LR, Antani SK, Thoma GR, Frazier SR, Moss RH, Stoecker WV. Nuclei-Based Features for Uterine Cervical Cancer Histology Image Analysis with Fusion-based Classification. DOI 10.1109/JBHI.2015.2483318. IEEE Journal of Biomedical and Health Informatics.