Jan Mycka

Accurate detection of prostate cancer suspicious areas in biparametric MRI (bpMRI) remains challenging because of severe lesion-to-background imbalance, limited lesion contrast, and inter-reader variability in lesion delineation. Unlike prior approaches that collapse inter-reader disagreement into a single consensus label, this study makes three contributions: (1) an adapted nnU-Net framework with prostate-centered preprocessing to reduce voxel-level class imbalance; (2) a class-imbalance-aware composite loss combining Dice, binary cross-entropy, and tailored focal loss to improve sensitivity to small and low-contrast lesions; and (3) a multi-expert learning strategy that preserves reader-specific annotations as separate supervision targets and aggregates predictions at the ensemble level. The method was developed on a single-center dataset of 378 bpMRI studies independently annotated by three board-certified radiologists. Of these, 323 studies were used for model development with patient-level 5-fold cross-validation, and 55 studies were reserved as a fixed independent test set. Compared with our previously published U-Net baseline, the proposed consensus-based nnU-Net improved Average Precision (AP) from 0.69 to 0.75, AUROC from 0.92 to 0.96, and the PI-CAI score from 0.81 to 0.85 on the independent test set. In addition, the multi-expert approach further improved AP to 0.81 versus 0.76 (+6.6%, p < 0.01), AUROC to 0.99 versus 0.95 (+4.2%, p < 0.01), and the PI-CAI score to 0.90 versus 0.86 (+4.7%). These findings demonstrate that explicitly preserving expert disagreement as a training signal, combined with anatomically targeted preprocessing and tailored loss design, substantially improves prostate lesion detection in bpMRI, providing a strong basis for future multi center external validation.

Music has always been an essential aspect of human culture, and the methods for its creation and analysis have evolved alongside the advancement of computational capabilities. With the emergence of artificial intelligence (AI) and one of its major goals referring to mimicking human creativity, the interest in music-related research has increased significantly. This review examines current literature from renowned journals and top-tier conferences, published between 2017 and 2023, regarding the application of AI to music-related topics. The study proposes a division of AI-in-music research into three major categories: music classification, music generation and music recommendation. Each category is segmented into smaller thematic areas, with detailed analysis of their inter- and intra-similarities and differences. The second part of the study is devoted to the presentation of the AI methods employed, with specific attention given to deep neural networks—the prevailing approach in this domain, nowadays. In addition, real-life applications and copyright aspects of generated music are outlined. We believe that a detailed presentation of the field along with pointing out possible future challenges in the area will be of some value for both the established AI-in-music researchers, as well as the new scholars entering this fascinating field.