I am a Computer Science Ph.D. Student (Eötvös Loránd University, Budapest, Hungary) and Senior Software Engineer passionate about using machine learning, deep learning, to solve challenging real world problems especially in the biomedical domain with strong scientific background and experience in research and industry.
In my PhD I focus on computer vision and deep learning for biomedical imaging. I currently work on developing weakly-supervised and self-supervised (SSL) deep learning models and methodologies for digital pathology and medicine.
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International contest about detection of TB bacilli on microscopy images. Collaborated with with Andras Biricz we developed an innovative solution based on transfer learning with novel vision encoders and self-supervised training.
🏆 Won the contest.
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International challenge focused on utilizing machine learning and deep learning to predict cancer stage solely from WSIs generated by breast biopsies. Teamed up with Andras Biricz we developed an powerful methodology based on a state-of-the-art hierarchical vision transformers architecture.
🏆 Won the contest.
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International challenge focused on utilizing machine learning and deep learning to predict cancer stage solely from WSIs generated by breast biopsies. Teamed up with Andras Biricz and Oz Kilim we created an efficient algorithm based on deep multi-instance learning and transfer learning.
🏆 Won the contest.
📰 Read more on the ELTE News pages here and here (hungarian only).
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Covid CXR - "Artificial Intelligence for Covid-19 prognosis: aiming at accuracy and explainability" Hackathon
The goal of the Hackaton was to predict Covid-19 severity from early chest X-ray imagines and clinical metadata with effective machine learning and deep learning models with focus on explainability. Participated with a team of four: Alex Olar, Andras Biricz, Bendeguz Sulyok.
🥈 2nd place overall. 🏆 1st prize for the most accurate model.