Research interests
I spend most of my time working on weakly-supervised learning from image datasets. I often employ a deep network in some way.
Contact
My email address is barucden [at] fel [dot] cvut [dot] cz.
My ORCID iD is 0000-0003-0428-3354.
Education
- 2019–present
- CTU in Prague, Ph.D., Machine learning with biomedical applications.
- 2017–2019
- CTU in Prague, Master (Ing.), Data science (with honors).
- 2014–2017
- CTU in Prague, Bachelor (Bc.), Software engineering (with honors).
Teaching experience
- Winter, 2022
- BAM33ZMO: Medical image analysis, 9 seminars.
- Winter, 2021
- B3B33ALP: Introduction to programming, 13 seminars.
- Winter, 2019 and 2020
- B4B33ALG: Basic data structures and algorithms, 13 seminars.
Research visits
- TU Dresden (6 months, December 2022)
- Learning generative models. Supervised by Dr. D. Schlesinger.
Publications
- D. Baručić, J. Kybic. Learning to segment from object thickness annotations. ISBI (IEEE, 2023).
- D. Baručić, et al. Characterization of drug effects on cell cultures from phase-contrast microscopy images. CIBM (Elsevier, 2022).
- D. Baručić, J. Kybic. Fast learning from label proportions with small bags. ICIP (IEEE, 2022).
- D. Baručić, J. Kybic. Learning to segment from object sizes. ITAT (CEUR-WS.org, 2022).
- D. Baručić, et al. Automatic evaluation of human oocyte developmental potential from microscopy images. SIPAIM (SPIE, 2021).
Minor contributions
- A. V. Le, et al. Improved recovery and annotation of genes in metagenomes through the prediction of fungal introns. Molecular Ecology Resources (Wiley, 2023).
- S. Kaushik, et al. The effect of hyperaldosteronism on carotid artery texture in ultrasound images. Diagnostics (MDPI, 2022).