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Tomasz Markiewicz
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2020 – today
- 2023
- [j11]Aleksandra Maria Osowska-Kurczab
, Tomasz Les
Improvement of renal image recognition through resolution enhancement. Expert Syst. Appl. 213(Part): 118836 (2023) - 2022
- [c23]Mateusz Lysik, Zaneta Swiderska-Chadaj
HE-to-PAS histological stain conversion by GAN in renal pathology. IJCNN 2022: 1-7 - 2021
- [j10]Tomasz Les, Tomasz Markiewicz, Miroslaw Dziekiewicz, Malgorzata Lorent:
Adaptive two-way sweeping method to 3D kidney reconstruction. Biomed. Signal Process. Control. 67: 102544 (2021) - [j9]Jaime Gallego, Zaneta Swiderska-Chadaj
A U-Net based framework to quantify glomerulosclerosis in digitized PAS and H&E stained human tissues. Comput. Medical Imaging Graph. 89: 101865 (2021) - [c22]Tomasz Les, Tomasz Markiewicz, Miroslaw Dziekiewicz, Malgorzata Lorent:
Kidney segmentation from computed tomography images using U-Net and batch-based synthesis. IJCNN 2021: 1-8 - 2020
- [c21]Tomasz Les, Tomasz Markiewicz, Miroslaw Dziekiewicz
Axis projection for Kidney-Region-Of-Interest detection in computed tomography. IJCNN 2020: 1-6 - [c20]Aleksandra Maria Osowska-Kurczab
Textural and deep learning methods in recognition of renal cancer types based on CT images. IJCNN 2020: 1-8 - [c19]Zaneta Swiderska-Chadaj, Karolina Nurzynska, Bartlomiej Grala, Katrien Grunberg, Lieke van der Woude, Monika Looijen-Salamon, Ann E. Walts, Tomasz Markiewicz, Francesco Ciompi, Arkadiusz Gertych
A deep learning approach to assess the predominant tumor growth pattern in whole-slide images of lung adenocarcinoma. Medical Imaging: Digital Pathology 2020: 113200D - [c18]Tomasz Les, Tomasz Markiewicz
Three-Dimensional Volumetric Renal Reconstruction Based on Geometrical Coefficients. MIE 2020: 458-462
2010 – 2019
- 2019
- [j8]Tomasz Les, Tomasz Markiewicz
Automatic reconstruction of overlapped cells in breast cancer FISH images. Expert Syst. Appl. 137: 335-342 (2019) - [c17]Zaneta Swiderska-Chadaj, Zhaoxuan Ma, Nathan Ing, Tomasz Markiewicz
Contextual Classification of Tumor Growth Patterns in Digital Histology Slides. ITIB 2019: 13-25 - 2018
- [j7]Zaneta Swiderska-Chadaj
Image processing methods for the structural detection and gradation of placental villi. Comput. Biol. Medicine 100: 259-269 (2018) - 2017
- [c16]Zaneta Swiderska-Chadaj
A Deep Learning Pipeline to Delineate Proliferative Areas of Intracranial Tumors in Digital Slides. MIUA 2017: 448-458 - 2016
- [j6]Tomasz Les
Fusion of FISH image analysis methods of HER2 status determination in breast cancer. Expert Syst. Appl. 61: 78-85 (2016) - [j5]Marek Wdowiak
Novel segmentation algorithm for identification of cell membrane staining in HER2 images. Pattern Recognit. Lett. 84: 225-231 (2016) - [c15]Zaneta Swiderska-Chadaj
Texture Analysis to Trophoblast and Villi Detection in Placenta Histological Images. ITIB (2) 2016: 183-192 - [c14]Tomasz Les
Dots Detection in HER2 FISH Images Based on Alternative Color Spaces. MIUA 2016: 132-137 - 2015
- [j4]Tomasz Markiewicz
Computerized System for Quantitative Assessment of Atherosclerotic Plaques in the Femoral and Iliac Arteries Visualized by Multislice Computed Tomography. IEEE Trans. Biomed. Eng. 62(6): 1490-1502 (2015) - [c13]Zaneta Swiderska
Texture and Mathematical Morphology for Hot-Spot Detection in Whole Slide Images of Meningiomas and Oligodendrogliomas. CAIP (2) 2015: 1-12 - [c12]Marek Wdowiak
Gradients and Active Contour Models for Localization of Cell Membrane in HER2/neu Images. CAIP (1) 2015: 432-444 - [c11]Zaneta Swiderska
Hot-spot selection and evaluation methods for whole slice images of meningiomas and oligodendrogliomas. EMBC 2015: 6252-6256 - [c10]Marek Wdowiak
Hourglass Shapes in Rank Grey-Level Hit-or-miss Transform for Membrane Segmentation in HER2/neu Images. ISMM 2015: 3-14 - 2014
- [j3]Tomasz Markiewicz
Recognition of atherosclerotic plaques and their extended dimensioning with computerized tomography angiography imaging. Int. J. Appl. Math. Comput. Sci. 24(1): 33-47 (2014) - [c9]Tomasz Les
Automatic Evaluation System of FISH Images in Breast Cancer. ICISP 2014: 332-339 - 2012
- [c8]Tomasz Staroszczyk, Stanislaw Osowski
Comparative Analysis of Feature Selection Methods for Blood Cell Recognition in Leukemia. MLDM 2012: 467-481
2000 – 2009
- 2009
- [j2]Stanislaw Osowski
Application of Support Vector Machine and Genetic Algorithm for Improved Blood Cell Recognition. IEEE Trans. Instrum. Meas. 58(7): 2159-2168 (2009) - [c7]Tomasz Markiewicz, Stanislaw Osowski, Cezary Jochymski, Joanna Narbutt, Wojciech Kozlowski:
Application of SVM for cell recognition in BCC skin pathology. ESANN 2009 - 2008
- [c6]Tomasz Markiewicz
Automatic cell recognition in immunohistochemical gastritis stains using sequential thresholding and SVM network. ISBI 2008: 971-974 - 2007
- [c5]Tomasz Markiewicz
White Blood Cell Automatic Counting System Based on Support Vector Machine. ICANNGA (2) 2007: 318-326 - 2006
- [c4]Tomasz Markiewicz, Stanislaw Osowski:
Data mining techniques for feature selection in blood cell recognition. ESANN 2006: 407-412 - 2005
- [c3]Stanislaw Osowski, Tran Haoi Linh, Tomasz Markiewicz:
Recognition of Heartbeats Using Support Vector Machine Networks - A Comparative Study. ICANN (2) 2005: 637-642 - [c2]Kazimierz Brudzewski, Stanislaw Osowski, Tomasz Markiewicz, Jan Ulaczyk:
Support Vector Machine for Recognition of Bio-products in Gasoline. ICANN (2) 2005: 899-904 - 2004
- [j1]Stanislaw Osowski
Support vector machine-based expert system for reliable heartbeat recognition. IEEE Trans. Biomed. Eng. 51(4): 582-589 (2004) - [c1]Stanislaw Osowski, Tomasz Markiewicz, Bozena Marianska, Leszek Moszczynski:
Feature generation for the cell image recognition of myelogenous leukemia. EUSIPCO 2004: 753-756
Coauthor Index
aka: Zaneta Swiderska-Chadaj
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