Oskari Lehtonen
Doctoral Researcher In Systems Biology Of Cancer at University of Helsinki
Helsinki, Mainland Finland
Summary
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Senior🎓
Top SchoolOskari Lehtonen is a doctoral researcher and AI engineer with eight years of experience building scalable deep learning and computer vision solutions for computational pathology. He designs panoptic and nuclei instance segmentation models, GPU‑accelerated whole‑slide image processing frameworks, and spatial analytics to extract interpretable histopathological features at scale. Comfortable in modern Python and PyTorch ecosystems, he maintains open‑source libraries that serve as core tooling for quantitative microscopy and WSI analysis. His background spans biomathematics, bioinformatics and chemistry, giving him a rare mix of domain knowledge and engineering rigor that informs both model design and biological interpretation.
8 years of coding experience
4 years of employment as a software developer
Master of Engineering - MEng, Biomathematics, Bioinformatics, and Computational Biology, Master of Engineering - MEng, Biomathematics, Bioinformatics, and Computational Biology at Aalto UniversityBachelor’s Degree, Chemistry, Bachelor’s Degree, Chemistry at University of Helsinki
English, Swedish, suomi
Stackoverflow
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Github Skills (22)
encoder10
encoder-decoder10
decoder10
benchmarking9
segmentation9
morphological-analysis9
computational-biology8
svd8
clinical-trials8
feature-extraction8
pytorch7
electrophysiology7
digital-pathology7
deep-learning6
spatial-data-analysis6
Programming languages (4)
Github contributions (5)
okunator/cellseg_models.pytorch
Jan 2022 - Jan 2023
Encoder-Decoder Cell and Nuclei segmentation models
Contributions:23 releases, 5 reviews, 234 commits in 11 months
decoderencodernuclei-segmentationencoder-decodernuclei
okunator/Dippa
Jul 2020 - Jan 2022
Benchmarking of deep learning and other segmentation methods for H&E images
Contributions:268 commits, 265 pushes, 2 branches in 1 year 6 months
deep-learningpytorchbenchmarkingsegmentation
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