Learning to predict the Ki67 proliferation index from histopathological images. – Bc. Adam Kukučka
Bc. Adam Kukučka
Bachelor's thesis
Learning to predict the Ki67 proliferation index from histopathological images.
Learning to predict the Ki67 proliferation index from histopathological images.
Abstract:
Súčasťou každodennej práce patológov je vyšetrovať stovky bioptických skenov. To je mnohokrát zdĺhavé, časovo náročné a náchylné na chyby. Technológie založené na pokroku v strojovom učení môžu patológom pomôcť znížiť ich pracovné zaťaženie. V tejto práci predstavujeme automatický výpočet proliferačného indexu Ki67, ktorý má výborné výsledky pri diagnostike rakoviny prsníka. Natrénovali sme konvolučnú …moreAbstract:
Pathologists’ day-to-day work is to examine hundreds of biopsy scans. This can be lengthy, time-consuming, and prone to errors. Technologies based on advances in machine learning may assist pathologists and reduce their workload. In this thesis, we present an automatic calculation of the Ki67 proliferation index, which has great results in breast cancer diagnosis. We train an end-to-end convolution …more
Language used: English
Date on which the thesis was submitted / produced: 23. 5. 2024
Identifier:
https://is.muni.cz/th/z9tdv/
Thesis defence
- Date of defence: 24. 6. 2024
- Supervisor: RNDr. Vít Musil, Ph.D.
- Reader: Anselm Paulus
Citation record
ISO 690-compliant citation record:
KUKUČKA, Adam. \textit{Learning to predict the Ki67 proliferation index from histopathological images.}. Online. Bachelor's thesis. Brno: Masaryk University, Faculty of Informatics. 2024. Available from: https://theses.cz/id/yron31/.
Full text of thesis
Contents of on-line thesis archive
Published in Theses:- světu
Other ways of accessing the text
Institution archiving the thesis and making it accessible: Masarykova univerzita, Fakulta informatikyMasaryk University
Faculty of InformaticsBachelor programme / field:
Informatics / Informatics
Theses on a related topic
-
Visualization of Digital Pathology Images and Results of Their Analyses Using Deep Neural Networks
Nikoleta Češeková -
AI Image Analysis Pipeline Implementation for Digital Pathology
Andrej Kubanda -
Prostate Cancer Prediction with Graph Neural Networks
Štěpán Řihák -
Implementace podpory formátu DICOM do AI systému RationAI
Radomír Dedek