Matej Halinkovic


I am a first year PhD student at FIIT STU in Bratislava, under the supervision of Prof. Wanda Benešová. My research focuses on multimodal deep learning applications of computer vision. I view the processing of heterogeneous sources of information, modalities, as a crucial step for advancing the universality of deep learning solutions. While originally this concept aimed to simulate the 5 human senses, I focus on applying it to combining various visual sensors for complex scene understanding. I believe that with the recent advancements in attention-based neural network architectures we can develop high-performance explainable solutions for even the most complex tasks.




  • Sloboda, T., Hudec, L., Halinkovic, M. and Benesova, W., 2024. Attention-Enhanced Unpaired xAI-GANs for Transformation of Histological Stain Images. Journal of Imaging, 10(2), p.32.
    In the field of heart transplantation, the ability to accurately and promptly diagnose cardiac allograft rejection 
    Histological staining is the primary method for confirming cancer diagnoses, but certain types, such as p63 staining, 
    can be expensive and potentially damaging to tissues. In our research, we innovate by generating p63-stained images 
    from H&E-stained slides for metaplastic breast cancer. This is a crucial development, considering the high costs and 
    tissue risks associated with direct p63 staining. Our approach employs an advanced CycleGAN architecture, xAI-CycleGAN, 
    enhanced with context-based loss to maintain structural integrity. The inclusion of convolutional attention in our model 
    distinguishes between structural and color details more effectively, thus significantly enhancing the visual quality 
    of the results. This approach shows a marked improvement over the base xAI-CycleGAN and standard CycleGAN models, offering 
    the benefits of a more compact network and faster training even with the inclusion of attention.
  • Kveton, M., Hudec, L., Vykopal, I., Halinkovic, M., Laco, M., Felsoova, A., Benesova, W. and Fabian, O., 2023. Digital Pathology in Cardiac Transplant Diagnostics: From Biopsies to Algorithms. Cardiovascular Pathology, p.107587.
    In the field of heart transplantation, the ability to accurately and promptly diagnose cardiac allograft rejection 
    is crucial. This comprehensive review explores the transformative role of digital pathology and computational 
    pathology, especially through machine learning, in this critical domain. These methodologies harness large datasets 
    to extract subtle patterns and valuable information that extend beyond human perceptual capabilities, potentially 
    enhancing diagnostic outcomes. Current research indicates that these computer-based systems could offer accuracy and 
    performance matching, or even exceeding, that of expert pathologists, thereby introducing more objectivity and reducing 
    observer variability. Despite promising results, several challenges such as limited sample sizes, diverse data sources, 
    and the absence of standardized protocols pose significant barriers to the widespread adoption of these techniques. 
    The future of digital pathology in heart transplantation diagnostics depends on utilizing larger, more diverse patient 
    cohorts, standardizing data collection, processing, and evaluation protocols, and fostering collaborative research 
    efforts. The integration of various data types, including clinical, demographic, and imaging information, could further
    refine diagnostic precision. As researchers address these challenges and promote collaborative efforts, digital 
    pathology has the potential to become an integral part of clinical practice, ultimately improving patient care in heart
  • Halinkovic, M., Mušková, K., Sloboda, T., Lepá?ek, M., Kan?árová, H., Ries, M. and Prnová, M.Š., 2024. MLtox, online phototoxicity prediction webpage. Toxicology in Vitro, 94, p.105701.
    Phototoxicity, sometimes in the literature referred to as photo-irritation, is a chemically induced reaction requiring 
    light. While it is generally accepted that phototoxicity testing can be performed in the majority of cases in vitro 
    (i.e. without the use of experimental animals), these tests may sometimes provide contradictory predictions. Understanding 
    the mechanisms of initiating events based on the molecule's structure and its ability to reach the excited state and 
    consequently generate ROS enables the creation of predictive QSAR for this adverse outcome. The ability to predict the 
    phototoxicity potential via a QSAR model is beneficial in reducing the number of mechanical in vitro/in chemico tests 
    needed to demonstrate absence of phototoxicity and it is very helpful in the overall safety assessment process.
    The QSAR prediction model presented here focused on developing a robust platform freely available on the web via the 
    link to provide interpretable predictions of the phototoxicity of tested molecules. Great attention 
    was devoted to interpretability and explainability of the prediction results. The web application allows the user to 
    input a chemical by CAS number, SMILES code or trivial name. The user can choose between simple prediction or advanced 
    tools options. These extended tools include the artificial intelligence explainability of model prediction using XSMILES 
    (interactive visualization technique to support the interpretation of SMILES) and SHAP values (impact each element on the 
    prediction). The comprehensive tools in question allow the user to explore the properties of phototoxic substances and to 
    understand the prediction outcomes better.
  • Halinkovic, M. and Benesova, W., 2022, August. SpringNet: A Novel Deep Neural Network Architecture for Histopathological Image Analysis. In The International Conference on Innovations in Computing Research (pp. 65-75). Cham: Springer International Publishing.
    Reliable and capable tools are needed for segmentation and classification of nuclei in histopathology images to 
    improve the workflow of histopathologists and lessen the burden of diagnosis. Analyzing histopathology images can 
    be challenging for deep learning models due to the various sources of noise. We propose a pipeline for segmenting 
    and classifying nuclei, trained on a combination of datasets to ensure robustness. This makes our pipeline capable 
    of generalizing on image tiles with varying Hematoxylin & Eosin concentrations. For nuclei classification, we present 
    a novel convolutional neural network classifier that demonstrate state-of-the-art classification performance on the 
    Lizard dataset.


  • Research methods of using deep neural networks in computer vision
    Halinkovic, Matej. Dissertation thesis: FIIT STU, ongoing
  • Interpretability and explainability of deep learning systems in the domain of microscopic medical imaging
    Halinkovic, Matej. Diploma thesis. Bratislava: FIIT STU, 2023.
  • Music Analysis Using Artificial Neural Networks
    Halinkovic, Matej. Bachelor thesis. Bratislava: FIIT STU, 2021.


  • Digital Processing of Sound, Image, and Biosignals – Teaching Assistant
  • Neural Networks – Teaching Assistant