Recent advancements in empirical and certified robustness have shown promising results in developing reliable and deployable Deep Neural Networks (DNNs). However, most evaluations of DNN robustness have focused on testing models on images from the same distribution they were trained on. In real-world scenarios, DNNs may encounter dynamic environments with significant distribution shifts. This thesis aims to investigate the interplay between empirical and certified adversarial robustness and domain generalization. We take the first step by training robust models on multiple domains and evaluating their accuracy and robustness on an unseen domain. Our findings reveal that: (1) both empirical and certified robustness exhibit generalization to unseen domains, and (2) the level of generalizability does not correlate strongly with the visual similarity of inputs, as measured by the Fréchet Inception Distance (FID) between source and target domains. Furthermore, we extend our study to a real-world medical application, where we demonstrate that adversarial augmentation significantly enhances robustness generalization while minimally affecting accuracy on clean data. This research sheds light on the importance of evaluating DNNs under real-world distribution shifts and highlights the potential of adversarial augmentation in improving robustness in practical applications.

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Yes / This article discusses the current evidence for the short- and long-term effects of concussion in sport and how occurrences of concussion should be managed. The article also considers the potential role of medical imaging in terms of assessing both acute and chronic head injuries. Greater awareness of when medical imaging could be used will aid the practitioner's understanding of its potential contribution while still maintaining the fundamental importance of clinical judgement.

Yes / Focus on patient experience and patient centred approaches within healthcare has substantially increased since the Picker Institute (a not for profit organisation) was established in the 1980′s [ [15] ]. The Picker Institute's vision for ‘the highest quality person centred care for all, always’ outlines eight principles of person-centred care which health care providers should strive for [ [15] ]: (1) Fast access to reliable healthcare advice [15]. (2) Effective treatment delivered by trusted professionals [15]. (3) Continuity of care and smooth transitions. [15] (4) Involvement and support for family and carers [15]. (5) Clear information, communication and support for self-care [15]. (6) Involvement in decisions and respect for preferences [15]. (7) Emotional support, empathy and respect [15]. (8) Attention to physical and environmental needs [15].

This dissertation explores the application of self-supervised learning methods in dental radiology to address the challenges posed by limited data availability for training deep learning models. The overarching goal is to enhance the efficiency and accuracy of automated systems for various dental diagnostic tasks, including teeth numbering, detection of dental restorations, orthodontic appliances, implant systems, marginal bone level, and dental caries from panoramic radiographs, CBCT images, intra-oral 3D scans, and dental radiographs. Key contributions include the development of several novel approaches: Self-supervised Learning for Dental Panoramic Radiographs: Utilizing SimMIM and UM-MAE with Swin Transformer, we achieved significant improvements in teeth detection and instance segmentation, increasing the average precision by 13.4% and 12.8%, respectively, over baseline methods. Self-Distillation Enhanced Self-supervised Learning (SD-SimMIM): Enhancing SimMIM with self-distillation loss, we improved performance on teeth numbering, dental restoration detection, and orthodontic appliance detection tasks, demonstrating superior outcomes compared to other methods. DentalMAE for Intra-oral 3D Scans: Extending the mesh masked autoencoder (MeshMAE), DentalMAE evaluates predicted deep embeddings of masked mesh triangles, yielding better generalization and higher accuracy in teeth segmentation tasks. DEMAE for Dental CBCT Images: Proposing the Deep Embedding MAE (DEMAE), which measures the closeness of predicted deep embeddings of masked patches to their originals, we achieved significant accuracy improvements in teeth segmentation from CBCT images. Masked Deep Embedding (MDE) for Implant Detection: By leveraging MIM, we developed MDE to enhance dental implant detection, creating a comprehensive Implant Design Dataset (IDD) with expert annotations, significantly boosting detection performance. Deep Embedding of Patches (DEP) for Bone Loss Assessment: An extension of MAE, DEP improved the accuracy of marginal bone level detection, supported by the creation of a Bone Loss Assessment Dataset (BLAD) with detailed annotations. Masked Deep Embedding of Patches (MDEP) for Caries Detection: This method enhanced dental caries detection performance, validated on the CariesXrays dataset, demonstrating higher precision and recall rates compared to traditional baselines. Through these innovations, the dissertation establishes the efficacy of self-supervised learning in overcoming data scarcity in dental imaging, offering promising AI-driven solutions for improved diagnostics and patient care in dentistry. / Computer and Information Science

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Computed Tomography (CT) is a medical imaging modality which acquires anatomical data via the unique x-ray attenuation of materials. Yet, some clinically important materials remain difficult to distinguish with current CT technology. Spectral CT is an emerging technology which acquires multiple CT datasets for specific x-ray spectra. These spectra provide a fingerprint that allow materials to be distinguished that would otherwise look the same on conventional CT. The unique characteristics of spectral CT data motivates research into novel visualization techniques. In this thesis, we aim to provide the foundation for visualizing spectral CT data. Our initial investigation of similar multi-variate data types identified intermixing as a promising visualization technique. This promoted the development of a generic, modular and extensible intermixing framework. Therefore, the contribution of our work is a framework supporting the construction, analysis and storage of algorithms for visualizing spectral CT studies. To allow evaluation, we implemented the intermixing framework in an application called MARSCTExplorer along with a standard set of volume visualization tools. These tools provide user-interaction as well as supporting traditional visualization techniques for comparison. We evaluated our work with four spectral CT studies containing materials indistinguishable by conventional CT. Our results confirm that spectral CT can distinguish these materials, and reveal how these materials might be visualized with our intermixing framework.

Medical volume segmentation got the attraction of many researchers; therefore, many techniques have been implemented in terms of medical imaging including segmentations and other imaging processes. This research focuses on an implementation of segmentation system which uses several techniques together or on their own to segment medical volumes, the system takes a stack of 2D slices or a full 3D volumes acquired from medical scanners as a data input. Two main approaches have been implemented in this research for segmenting medical volume which are multi-resolution analysis and statistical modeling. Multi-resolution analysis has been mainly employed in this research for extracting the features. Higher dimensions of discontinuity (line or curve singularity) have been extracted in medical images using a modified multi-resolution analysis transforms such as ridgelet and curvelet transforms. The second implemented approach in this thesis is the use of statistical modeling in medical image segmentation; Hidden Markov models have been enhanced here to segment medical slices automatically, accurately, reliably and with lossless results. But the problem with using Markov models here is the computational time which is too long. This has been addressed by using feature reduction techniques which has also been implemented in this thesis. Some feature reduction and dimensionality reduction techniques have been used to accelerate the slowest block in the proposed system. This includes Principle Components Analysis, Gaussian Pyramids and other methods. The feature reduction techniques have been employed efficiently with the 3D volume segmentation techniques such as 3D wavelet and 3D Hidden Markov models. The system has been tested and validated using several procedures starting at a comparison with the predefined results, crossing the specialists’ validations, and ending by validating the system using a survey filled by the end users explaining the techniques and the results. This concludes that Markovian models segmentation results has overcome all other techniques in most patients’ cases. Curvelet transform has been also proved promising segmentation results; the end users rate it better than Markovian models due to the long time required with Hidden Markov models.

Bakgrund: Ett sjukhus i mellersta Sverige utför samordnade undersökningar i samband med när barnet behöver sövas inför en magnetisk resonanstomografi (MR) undersökning. Detta koordineras och bokas av en röntgensjuksköterska som därefter skickar ut en kallelse till patienten. Syfte: Att belysa hur föräldrar upplever att flera olika undersökningar utförs på deras barn i samband med sövning vid MR-undersökning. Metod: Studien har en kvalitativ ansats där enskilda intervjuer med föräldrarna har skett. Urvalet för studien bestod av åtta föräldrar vars barn blev sövda inför en MR-undersökning där fler andra undersökningar gjordes vid samma tillfälle. Resultat: Informanterna var helt eniga om att de samordnade undersökningarna var något positivt. En viktig aspekt som många av informanterna nämnde var bemötandet från vårdpersonalen vilket majoriteten var nöjda med. Mindre nöjda var några informanter kring informationen angående MR. Slutsats: Generellt är föräldrarna nöjda med samordnade undersökningar. Ett informationsbrev kring hur MR-undersökningen utförs kan skickas med i kallelsen för att förbereda båda barn och föräldrar.