1. Benčević, M., Habijan, M., Galić, I., Babin, D., & Pižurica, A. (2024). Understanding skin color bias in deep learning-based skin lesion segmentation. Computer methods and programs in biomedicine.
2. Vercheval, N., Benčević, M., Mužević, D., Galić, I., & Pižurica, A. (2023). Counterfactual Functional Connectomes for Neurological Classifier Selection. 2023 31st European Signal Processing Conference (EUSIPCO).
3. Benčević, M., Qiu, Y., Galić, I., & Pižurica, A. (2023). Segment-then-segment: context-preserving crop-based segmentation for large biomedical images. Sensors.
4. 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.
5. 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.
6. 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.
7. 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.
8. Benčević, M., Habijan, M., & Galić, I. (2021). Epicardial adipose tissue segmentation from CT images with a semi-3D neural network. 2021 International Symposium ELMAR.
9. Benčević, M., Galić, I., Habijan, M., & Babin, D. (2021). Training on polar image transformations improves biomedical image segmentation. IEEE access.

1. Data Efficient Deep Learning Models for Biomedical Image Segmentation, Marin Benčević, 2024.