Pingfu Fu
Orcid: 0000-0002-2334-5218
According to our database1,
Pingfu Fu
authored at least 11 papers
between 2019 and 2024.
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Bibliography
2024
Rank acquisition impact on radiomics estimation (AсquIRE) in chest CT imaging: A retrospective multi-site, multi-use-case study.
Comput. Methods Programs Biomed., 2024
Deep learning reveals lung shape differences on baseline chest CT between mild and severe COVID-19: A multi-site retrospective study.
Comput. Biol. Medicine, 2024
2023
CoRR, 2023
STAR-Echo: A Novel Biomarker for Prognosis of MACE in Chronic Kidney Disease Patients Using Spatiotemporal Analysis and Transformer-Based Radiomics Models.
Proceedings of the Medical Image Computing and Computer Assisted Intervention - MICCAI 2023, 2023
2022
Novel Radiomic Measurements of Tumor- Associated Vasculature Morphology on Clinical Imaging as a Biomarker of Treatment Response in Multiple Cancers.
CoRR, 2022
2021
Integrated Clinical and CT Based Artificial Intelligence Nomogram for Predicting Severity and Need for Ventilator Support in COVID-19 Patients: A Multi-Site Study.
IEEE J. Biomed. Health Informatics, 2021
LuMiRa: An Integrated Lung Deformation Atlas and 3D-CNN Model of Infiltrates for COVID-19 Prognosis.
Proceedings of the Medical Image Computing and Computer Assisted Intervention - MICCAI 2021 - 24th International Conference, Strasbourg, France, September 27, 2021
2020
Deep learning-based prediction of response to HER2-targeted neoadjuvant chemotherapy from pre-treatment dynamic breast MRI: A multi-institutional validation study.
CoRR, 2020
Computer extracted features related to the spatial arrangement of tumor-infiltrating lymphocytes predict overall survival in epithelial ovarian cancer receiving adjuvant chemotherapy.
Proceedings of the Medical Imaging 2020: Digital Pathology, 2020
2019
A combination of intra- and peritumoral features on baseline CT scans is associated with overall survival in non-small cell lung cancer patients treated with immune checkpoint inhibitors: a multi-agent multi-site study.
Proceedings of the Medical Imaging 2019: Computer-Aided Diagnosis, 2019
Quantitative vessel tortuosity radiomics on baseline non-contrast lung CT predict response to immunotherapy and are prognostic of overall survival.
Proceedings of the Medical Imaging 2019: Computer-Aided Diagnosis, 2019