This thesis utilizes Evolutionary Algorithms (EAs) within the BRIGHT framework for developing breast cancer brachytherapy treatment plans. We use expert knowledge and state-of-the-art EAs to formulate treatment planning as a multi-objective optimization problem whose solutions can be applied to actual patients. We propose four novel 2- and 3-objective formulations of this problem, which we implement within BRIGHT and empirically validate against anonymized data from 9 real-world patient cases. We demonstrate that all four formulations, under reasonable computational and time budgets, are capable of generating plans that match or exceed the properties of reference treatment plans. To verify the clinical relevance of our contributions, we rely on the expertise of a clinical expert who assesses whether the generated plans can be used in clinical treatment planning. The results of our empirical analysis show that 17 of the 18 plans presented to the expert are clinically acceptable and of immediate value to practitioners within the field of breast brachytherapy today.

Counterfactual explanations are a useful tool to explain trained models. They are based on counterfactual thoughts, which are a natural human thought process that helps us reason about the past. When applied to trained models they show how to make minimal changes to a data point in order to obtain a desired output.
Most methods find these counterfactuals by optimizing a set of objectives. Previously these objectives were often combined into a loss function using an aggregation operator. This operator implicitly decides the priority between the objectives, but this ordering is not always in line with the user’s preferences.
To mitigate this the Multi-Objective Counterfactuals (MOC) method was introduced. MOC turns counterfactual generation into a multi-objective optimization problem and presents the user with a diverse set of counterfactuals that have different trade-offs for the objectives. It optimizes the set of objectives with an evolutionary algorithm called Nondominated Sorting Genetic Algorithm II.
In this thesis we optimize this problem using Multi-Objective Real-Valued Gene-Pool Optimal Mixing Evolutionary Algorithm, which is a different evolutionary algorithm. We present a single-modal method and two multi-modal methods. We compare the performance of our methods to a counterfactual generation method named Diverse Counterfactual Explanations (DiCE), which focusses on feasibility and diversity within a set of generated counterfactuals. Additionally, we also present a visualization tool for sets of counterfactuals.
The single-modal method generates counterfactuals that are realistic, but do not consistently perform well in other areas. The first multi-modal method generates diverse sets of counterfactuals, but overall performs worse. The second multi-modal method generates counterfactuals that perform similarly to the single-modal method, but are more diverse.

Machine learning (ML) models are used increasingly in high-stakes areas such as health and finance because of their strong performance. However, having good performance in metrics such as accuracy or the f1 score alone is not all that is important as trust is also essential in these areas. TalkToModel is a system that addresses this challenge, using a large language model (LLM), by letting the users interact with their models through natural language. However, this system only allows a user to ask black-box questions. Another way to achieve trust is through models that are constructed with interpretability in mind that a human can understand. Genetic programming (GP) models are such models that have the potential to be interpreted. This thesis investigates if GP models can be made even more interpretable
using TalkToModel. To do this an enhanced version of TalkToModel called TalkToGP is created with three main contributions: 1) Integration of GP Models into TalkToModel, 2) the ability to ask GP modelspecific questions and 3) the ability to do a comparative analysis between multiple GP models. This system is built using the feedback from GP users who gave insights on their experience with GP as well as their wishes for this system. In the end, the system is evaluated by GP users in an experiment. The experiments showed that the enhanced version of TalkToModel shows a strong indication that it increases the interpretability of GP models. This means the system could be a useful tool for anyone working with GP models.

Deformable Image Registration (DIR) is a process in which the point-to-point correspondence between two or more medical images is estimated. This could allow spatial data to be transferred between these images, easing the work of practitioners in the field of radiation oncology. Many DIR approaches already exist. These are not yet applicable in all clinical settings, however, as DIR is a complex problem, partly because of the difficulty of evaluating image registration results. This is why often multiple objectives are considered in this evaluation. In this thesis, we combine two separate multi-objective DIR approaches into hybrids while evaluating their performances, with the aim to improve upon the individual approaches, thus reducing the gap toward clinical practice. In these hybrids, we also use an approach called Automated Landmark Detection which automatically detects corresponding landmarks between the source and target images.

The first DIR approach, the Digital Phantom, creates a biomechanical model of the patient and uses an Evolutionary Algorithm (EA) to optimize parameters of a Finite Element Method (FEM)-based simulation. The second DIR approach, MOREA, creates a dual-dynamic biomechanical transformation model of the patient, using an EA to optimize its parameters.

In the first hybrid approach, called the Landmark Guided Digital Phantom (LGDP), the Automated Landmark Detection approach is used to automatically find corresponding landmarks, which are then used as a guidance objective in the Digital Phantom. The second hybrid is called DP-MOREA, where the output of the Digital Phantom is used to initialize deformations in the MOREA approach. The final hybrid is called LGDP-MOREA. This hybrid combines all three components, using the generated landmarks to guide the Digital Phantom and initializing MOREA with the result. We analyze the effect of these hybrids on the quality of the final registration, both quantitatively and qualitatively. Additionally, we compare these hybrids with the components they are composed of.

We test the performance of the approaches and hybrids on pelvic CT scans of three cervical cancer patients with large deformations. We observe that the individual Digital Phantom component outperforms the LGDP hybrid both quantitatively and qualitatively on these three patients. In addition, we observe that initializing MOREA with the output of the Digital Phantom and LGDP improves on how fast the optimization converges, that it improved the end results quantitatively and that it also has an effect on the resulting Deformation Vector Fields (DVFs). We conclude that in future research, these effects on the DVFs and their clinical relevance should be studied in more detail.