Areas of Scientific Interest
Digital pathology. Predictive models of disease. Medical informatics. Machine learning. AI solutions.
Main Activities / Research Directions
Consolidate the academic potential of digital medicine for artificial intelligence solutions:
- Facilitate interdisciplinary collaboration at Vilnius University and with external partners.
Develop training modules in health informatics.
Promote the development of digital medicine science and technology:
- Initiate potentially high-added-value projects by integrating multimodal data sets and improving their quality.
- Consult on aspects of health data management and use, initiating projects and participating in them, if necessary.
Develop niche technologies that can provide unique value to digital medicine models:
- Develop research on digital/computational biomarkers in pathology, radiology and other fields.
- Develop multimodal microscopy imaging technologies with a perspective for clinical application.
Research
PhD students:
Vygantė Maskoliūnaitė. Prognostic Modeling of Primary Cutaneous Melanoma by Digital Pathology and Machine Learning Methods (Academic supervisor: Prof. Dr. Arvydas Laurinavičius).
Mantas Fabijonavičius. Machine Learning-Driven Assessment of Renal Cancer Microenvironment for Prognostic Modeling (Academic supervisor: Prof. Dr. Arvydas Laurinavičius).
Renaldas Augulis. Artificial Intelligence-Enabled Kidney Histomorphometrics for Clinical and Experimental Research (Academic supervisor: Prof. Dr. Arvydas Laurinavičius).
Julius Drachneris. Modeling of Urothelial Bladder Carcinoma Microenvironment by Digital Pathology and Artificial Intelligence Methods (Academic supervisor: Prof. Dr. Arvydas Laurinavičius).
Master theses:
Emilija Keževičiūtė. Utilization of Multimodal Microscopy Data for Machine Learning by Integrating Optical, Polarization, and Fluorescence Microscopy Data (Academic supervisor: Prof. Dr. Arvydas Laurinavičius).
Marx Sören. Assessment of Tumor Infiltrating Lymphocytes by Machine Learning Methods (Academic supervisor: Prof. Dr. Arvydas Laurinavičius).
Vilhelmas Landsbergis. Machine Learning Algorithms Based on Polychromatic Polarization Microscopy Data for Tissue Pathology Evaluation (Academic supervisor: Prof. Dr. Arvydas Laurinavičius).
Juras Jocys. Artificial Intelligence Solutions for Skin Pathology (Academic supervisor: Prof. Dr. Arvydas Laurinavičius).
Marx Sören. Assessment of tumor infiltrating lymphocytes by machine learning methods (Academic supervisor: Prof. Dr. Arvydas Laurinavičius).
Edgaras Zaboras. Multimodal (Bright Field, Polarized, Fluorescence) Microscopy for Machine Learning (Academic supervisor: Prof. Dr. Arvydas Laurinavičius).
Methods and Infrastructure Used
Leica Aperio virtual microscopy.
Polychromatic polarized microscopy.
HALO AP/ HALO AI image analysis platform.
Statistical analysis platform, SAS.
Computational platform, NVIDIA GPU.
Projects
Polychromatic Polarization Microscopy for Machine Learning in Tissue Pathology (PPM4ML), Lithuanian Research Council, Researcher groups project, 2024-2027.
Data Center for Machine Learning and Quantum Computing in Natural and Biomedical Sciences (Project no.: S-A-UAI-23-11)/MF involved in "Center of Excellence in Digital Medicine" (SMEC), LR budget (LMT, ŠMSM), 2023-2027.
Collaboration
Vilnius University Hospital Santaros klinikos.
Vilnius University Faculty of Physics.
Vilnius University Faculty of Mathematics and Informatics.
Lithuanian Association of Artificial Intelligence.
Marine Biological Laboratory.
University of Caen Normandy.
List of Employees and Contacts
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Academic degree |
Name |
Memberships in societies |
Contacts |
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Prof. MD PhD
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ESDIP (Advisory Board)
Journal of Pathology Informatics (Editorial Board) |
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Dr. PhD
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Dr. PhD
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Dr. PhD
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PhD candidate |
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PhD candidate |
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Martynas Bieliauskas |
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PhD |
Julius Juodakis |
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PhD |
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PhD |
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Benoit Plancoulaine |
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PhD |
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Dr. PhD |
Kanapeckaitė Austė |
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