Patrick Leo
Orcid: 0000-0002-2248-0610
According to our database1,
Patrick Leo
authored at least 13 papers
between 2016 and 2024.
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Bibliography
2024
Automatic myeloblast segmentation in acute myeloid leukemia images based on adversarial feature learning.
Comput. Methods Programs Biomed., January, 2024
2022
Novel Radiomic Measurements of Tumor- Associated Vasculature Morphology on Clinical Imaging as a Biomarker of Treatment Response in Multiple Cancers.
CoRR, 2022
2021
Feature-driven local cell graph (FLocK): New computational pathology-based descriptors for prognosis of lung cancer and HPV status of oropharyngeal cancers.
Medical Image Anal., 2021
SPARTA: An Integrated Stability, Discriminability, and Sparsity Based Radiomic Feature Selection Approach.
Proceedings of the Medical Image Computing and Computer Assisted Intervention - MICCAI 2021 - 24th International Conference, Strasbourg, France, September 27, 2021
2020
Texture features distinguish benign cell clusters from adenocarcinomas on bile duct brushing cytology images.
Proceedings of the Medical Imaging 2020: Digital Pathology, 2020
Three-dimensional histo-morphometric features from light sheet microscopy images result in improved discrimination of benign from malignant glands in prostate cancer.
Proceedings of the Medical Imaging 2020: Digital Pathology, 2020
Compactness measures of tumor infiltrating lymphocytes in lung adenocarcinoma are associated with overall patient survival and immune scores.
Proceedings of the Medical Imaging 2020: Digital Pathology, 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
Computationally derived cytological image markers for predicting risk of relapse in acute myeloid leukemia patients following bone marrow transplantation.
Proceedings of the Medical Imaging 2020: Digital Pathology, 2020
2019
Radiomic features derived from pre-operative multi-parametric MRI of prostate cancer are associated with Decipher risk score.
Proceedings of the Medical Imaging 2019: Computer-Aided Diagnosis, 2019
2018
Combination of nuclear NF-κB/p65 localization and gland morphological features from surgical specimens appears to be predictive of early biochemical recurrence in prostate cancer patients.
Proceedings of the Medical Imaging 2018: Digital Pathology, 2018
Empirical evaluation of cross-site reproducibility in radiomic features for characterizing prostate MRI.
Proceedings of the Medical Imaging 2018: Computer-Aided Diagnosis, 2018
2016
Evaluating stability of histomorphometric features across scanner and staining variations: predicting biochemical recurrence from prostate cancer whole slide images.
Proceedings of the Medical Imaging 2016: Digital Pathology, San Diego, California, United States, 27 February, 2016