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Arkadiusz Gertych

Showing results (41-50 of 59) with videos related to

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Diagnostic Pathology|September 20, 2017
Data integration from pathology slides for quantitative imaging of multiple cell types within the tumor immune cell infiltrateZhaoxuan Ma, Stephen L Shiao, Emi J Yoshida, et al.
Journal of Orthopaedic Research : Official Publication of the Orthopaedic Research Society|October 7, 2022
Directing iPSC differentiation into iTenocytes using combined scleraxis overexpression and cyclic loadingAngela Papalamprou, Victoria Yu, Angel Chen, et al.
Frontiers in Oncology|April 7, 2022
Focal Serous Tubal Intra-Epithelial Carcinoma Lesions Are Associated With Global Changes in the Fallopian Tube Epithelia and StromaJingni Wu, Yael Raz, Maria Sol Recouvreux, et al.
Frontiers in Oncology|March 15, 2021
Predicting Metastasis Risk in Pancreatic Neuroendocrine Tumors Using Deep Learning Image AnalysisSergey Klimov, Yue Xue, Arkadiusz Gertych, et al.
Scientific Reports|February 8, 2019
Convolutional neural networks can accurately distinguish four histologic growth patterns of lung adenocarcinoma in digital slidesArkadiusz Gertych, Zaneta Swiderska-Chadaj, Zhaoxuan Ma, et al.
The American Journal of Pathology|February 1, 2025
Tumor Cellularity Assessment Using Artificial Intelligence Trained on Immunohistochemistry-Restained Slides Improves Selection of Lung Adenocarcinoma Samples for Molecular TestingArkadiusz Gertych, Natalia Zurek, Natalia Piaseczna, et al.
Computerized Medical Imaging and Graphics : the Official Journal of the Computerized Medical Imaging Society|September 13, 2015
Machine learning approaches to analyze histological images of tissues from radical prostatectomiesArkadiusz Gertych, Nathan Ing, Zhaoxuan Ma, et al.
Breast Cancer Research : BCR|July 31, 2019
A whole slide image-based machine learning approach to predict ductal carcinoma in situ (DCIS) recurrence riskSergey Klimov, Islam M Miligy, Arkadiusz Gertych, et al.
Computers in Biology and Medicine|February 19, 2018
Optimized multi-level elongated quinary patterns for the assessment of thyroid nodules in ultrasound imagesU Raghavendra, Anjan Gudigar, M Maithri, et al.
Scientific Reports|October 18, 2017
A novel machine learning approach reveals latent vascular phenotypes predictive of renal cancer outcomeNathan Ing, Fangjin Huang, Andrew Conley, et al.
Pageof 6

Showing results (41-50 of 59) with videos related to

Sort By:
Pageof 6
Diagnostic Pathology|September 20, 2017
Data integration from pathology slides for quantitative imaging of multiple cell types within the tumor immune cell infiltrateZhaoxuan Ma, Stephen L Shiao, Emi J Yoshida, et al.
Journal of Orthopaedic Research : Official Publication of the Orthopaedic Research Society|October 7, 2022
Directing iPSC differentiation into iTenocytes using combined scleraxis overexpression and cyclic loadingAngela Papalamprou, Victoria Yu, Angel Chen, et al.
Frontiers in Oncology|April 7, 2022
Focal Serous Tubal Intra-Epithelial Carcinoma Lesions Are Associated With Global Changes in the Fallopian Tube Epithelia and StromaJingni Wu, Yael Raz, Maria Sol Recouvreux, et al.
Frontiers in Oncology|March 15, 2021
Predicting Metastasis Risk in Pancreatic Neuroendocrine Tumors Using Deep Learning Image AnalysisSergey Klimov, Yue Xue, Arkadiusz Gertych, et al.
Scientific Reports|February 8, 2019
Convolutional neural networks can accurately distinguish four histologic growth patterns of lung adenocarcinoma in digital slidesArkadiusz Gertych, Zaneta Swiderska-Chadaj, Zhaoxuan Ma, et al.
The American Journal of Pathology|February 1, 2025
Tumor Cellularity Assessment Using Artificial Intelligence Trained on Immunohistochemistry-Restained Slides Improves Selection of Lung Adenocarcinoma Samples for Molecular TestingArkadiusz Gertych, Natalia Zurek, Natalia Piaseczna, et al.
Computerized Medical Imaging and Graphics : the Official Journal of the Computerized Medical Imaging Society|September 13, 2015
Machine learning approaches to analyze histological images of tissues from radical prostatectomiesArkadiusz Gertych, Nathan Ing, Zhaoxuan Ma, et al.
Breast Cancer Research : BCR|July 31, 2019
A whole slide image-based machine learning approach to predict ductal carcinoma in situ (DCIS) recurrence riskSergey Klimov, Islam M Miligy, Arkadiusz Gertych, et al.
Computers in Biology and Medicine|February 19, 2018
Optimized multi-level elongated quinary patterns for the assessment of thyroid nodules in ultrasound imagesU Raghavendra, Anjan Gudigar, M Maithri, et al.
Scientific Reports|October 18, 2017
A novel machine learning approach reveals latent vascular phenotypes predictive of renal cancer outcomeNathan Ing, Fangjin Huang, Andrew Conley, et al.
Pageof 6