Tomasz Les
Warsaw University of Technology
19 Papers
46 Citations
Tomasz Les is an academic researcher from Warsaw University of Technology. The author has contributed to research in topics: Image processing & Image segmentation. The author has an hindex of 4, co-authored 17 publications. Previous affiliations of Tomasz Les include Military University of Technology in Warsaw.
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Papers
Automatic Evaluation System of FISH Images in Breast Cancer
Tomasz Les,Tomasz Markiewicz,Stanislaw Osowski,Stanislaw Osowski,Marzena Cichowicz,Wojciech Kozłowski +5 more
- 30 Jun 2014
TL;DR: The paper presents the algorithm of an automatic evaluation of the fluorescent in situ hybridization (FISH) images in order to determine HER2 status of the breast cancer samples based on the accurate measurement of the red/green spot ratio (the ratio of HER2/CEN17) per cell nucleus.
Automatic recognition of industrial tools using artificial intelligence approach
TL;DR: The paper presents an automatic approach to the recognition of the industrial tools on the basis of their image registered by the camera and the developed system has been verified on the example of exemplary set of industrial tools.
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Fusion of FISH image analysis methods of HER2 status determination in breast cancer
TL;DR: The paper presents an application of several methods of markers localization in fluorescent in situ hybridization images in order to determine HER2 status of the breast cancer samples and proposes their fusion to obtain better results of spot recognition.
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Localization of spots in FISH images of breast cancer using 3-D shape analysis.
Tomasz Les,Tomasz Markiewicz,Stanislaw Osowski,Stanislaw Osowski,Marzena Jesiotr,Wojciech Kozłowski +5 more
TL;DR: The proposed method of FISH image analysis improves the efficiency of detecting fluorescent signals in FISH images and is encouraging for further testing of the developed automatic system directed to application in medical practice.
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Kidney Boundary Detection Algorithm Based on Extended Maxima Transformations for Computed Tomography Diagnosis
TL;DR: The proposed kidney recognition system can be successfully used in systems that require a very fast image processing time, and can generate the kidney boundary up to 3 times faster than raw U-Net-based network.
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