1. Benčević, M., Qiu, Y., Galić, I., & Pižurica, A. (2023). Segment-then-Segment: Context-Preserving Crop-Based Segmentation for Large Biomedical Images. Sensors.
  2. Benčević, M., Habijan, M., Galić, I., & Pizurica, A. (2022). Self-supervised Learning as a Means to Reduce the Need for Labeled Data in Medical Image Analysis. EUSIPCO 2022, 30th European Signal Processing Conference.
  3. Benčević, M., Habijan, M., Galić, I., & Babin, D. (2022). Using the Polar Transform for Efficient Deep Learning-Based Aorta Segmentation in CTA Images. 2022 International Symposium ELMAR.
  4. Benčević, M., Galić, I., Habijan, M., & Pižurica, A. (2022). Recent Progress in Epicardial and Pericardial Adipose Tissue Segmentation and Quantification Based on Deep Learning: A Systematic Review. Applied Sciences.
  5. Leventic, H., Bencevic, M., Babin, D., & Habijan, M. (2021). A Survey of Left Atrial Appendage Segmentation and Analysis in 3D and 4D Medical Images. The 28th International Conference on Systems, Signals and Image Processing.
  6. Benčević, M., Habijan, M., & Galić, I. (2021). Epicardial Adipose Tissue Segmentation from CT Images with A Semi-3D Neural Network. 2021 International Symposium ELMAR.
  7. Benčević, M., Galić, I., Habijan, M., & Babin, D. (2021). Training on polar image transformations improves biomedical image segmentation. IEEE Access.