Advanced methods of medical image analysis for breast cancer risk predictionKlaneček, Žan (Avtor) Jeraj, Robert (Mentor) breast cancerbreast cancer riskartificial intelligencemachine learningdeep learninguncertainty estimationsensitivityinterpretabilitysegmentationpectoral musclepersonalized screeningmammographyBreast cancer screening programs are generally effective; however, it remains unclear whether the uniform screening interval applied to all women is optimal. Personalized screening—tailoring the frequency to an individual's breast cancer risk (BCR)—may provide a better solution. For the realization of personalized BCR screening, this thesis focuses on developing a deep learning (DL)-based BCR prediction model specifically optimized for the Slovenian population, leveraging all relevant information available from routine 2D mammograms within screening programs. Initially, the state-of-the-art BCR prediction model, MIRAI, was validated on Slovenian screening data, achieving performance comparable to that reported at other screening centers. The evaluation also revealed that MIRAI occasionally bases its predictions on the pectoral muscle (PM) region in mammograms without clear clinical justification. To address this, we hypothesized that excluding the PM region during model training would improve predictive performance. Consequently, a PM segmentation model incorporating an uncertainty-based flagging mechanism using Monte Carlo dropout was developed and validated. Fine-tuning experiments confirmed our hypothesis: BCR prediction models trained on mammograms with the PM region removed demonstrated superior discrimination in predicting 1–5-year BCR on Slovenian data. Recognizing that these models could be sensitive to changes introduced by the physics-based principles of image acquisition and biological variations, we systematically quantified their impact through controlled experiments simulating realistic image alterations. Finally, to increase trust and clinical acceptance, a longitudinal interpretability study examined how the model’s reliance on the cancer-affected breast side evolves over time. In summary, the findings and technical innovations presented in this thesis contribute significantly to advancing personalized breast cancer screening in Slovenia. However, prior to the implementation of DL-based BCR screening in clinical practice, results from prospective clinical trials are needed.20252025-07-07 12:52:47Doktorsko delo/naloga170501VisID: 150714COBISS_ID: 244299523sl