Crafting and refining high-dose-rate brachytherapy treatment plans for cervical cancer is a time-consuming process. In recent years, BRIGHT was developed, an AI-based automated treatment planning method that provides not just one, but a set of optimized, patient-specific treatment plans, each with a different trade-off between objectives of interest. BRIGHT's plans are optimized using protocols that define guidelines on the delivered doses. In this thesis, we explore an alternative approach using dose-response models. These models provide insights into the estimated outcomes and risks of a treatment plan. Additionally, they offer great adaptability by including external patient characteristics. Currently, the use of these models remains limited to a feedback role. However, we instead include these models in BRIGHT to optimize them directly. Using one Tumor Control Probability (TCP) model and five Normal-Tissue Complication Probability (NTCP) models, we designed and tested several dose-response objective formulations and optimization techniques. We found that with the current models, these new objectives are insufficient as a replacement for BRIGHT's protocol-based coverage and sparing objectives. The produced plans have greatly improved dose-response outcomes but fall short in protocol compliance. Extending the existing objectives rather than replacing them proved more favorable. Average model improvements around 0.004 for NTCP are observed among the best coverage-sparing plans satisfying the protocol. Additionally, by sacrificing some sparing in the protocol-satisfaction range, improvements around 0.005 are possible for TCP and NTCP. Moreover, these dose-response-focused plans show distinct differences in their dose distribution favoring the dose-response targets compared to regular BRIGHT. Ultimately, the improvements we obtained are only marginal, and the clinical implications of this are unclear. The covariates of the models used in this thesis mostly overlapped with BRIGHT's objectives and did not fully extract their potential. Nevertheless, this thesis proves that the concept is viable and builds a foundation for this technique for when more and better dose-response models become available.

Gaze estimation is an important area of research used in a wide range of applications. However, existing models trained for gaze estimation often suffer from high computational costs. In this study, frequency domain channel selection techniques were explored to decrease these costs by reducing the size of the input data. The main research objective was to investigate the impact of channel selection on the latency and accuracy of frequency domain gaze estimation. Channel selection methods used in related research were adapted and applied to the domain of gaze estimation. The evaluation was conducted on two popular network architectures used in this field, namely the AlexNet and ResNet-18. Multiple channel selection models were designed for each architecture and compared to a traditional RGB approach with the same network structure. Experimental results showed significant speedups during training, calibration, and inference with marginal accuracy loss. The specific speedups that the top-performing models of both the architectures achieves were 3.3, 4.0, and 1.35 for the AlexNet, and 1.5, 1.7, and 1.35 for the ResNet-18. Accompanying these speedups the AlexNet model error only increased by 0.08 degrees compared to a traditional RGB approach, while the ResNet-18 model lost around 0.44 degrees. All the code used in this research is publicly available on GitHub (https://github.com/tpenning/DLFDFaceGazeEstimation).