Unsupervised learning of Arabic non-concatenative morphology

Unsupervised approaches to learning the morphology of a language play an important role in computer processing of language from a practical and theoretical perspective, due their minimal reliance on manually produced linguistic resources and human annotation. Such approaches have been widely researched for the problem of concatenative affixation, but less attention has been paid to the intercalated (non-concatenative) morphology exhibited by Arabic and other Semitic languages. The aim of this research is to learn the root and pattern morphology of Arabic, with accuracy comparable to manually built morphological analysis systems. The approach is kept free from human supervision or manual parameter settings, assuming only that roots and patterns intertwine to form a word. Promising results were obtained by applying a technique adapted from previous work in concatenative morphology learning, which uses machine learning to determine relatedness between words. The output, with probabilistic relatedness values between words, was then used to rank all possible roots and patterns to form a lexicon. Analysis using trilateral roots resulted in correct root identification accuracy of approximately 86% for inflected words. Although the machine learning-based approach is effective, it is conceptually complex. So an alternative, simpler and computationally efficient approach was then devised to obtain morpheme scores based on comparative counts of roots and patterns. In this approach, root and pattern scores are defined in terms of each other in a mutually recursive relationship, converging to an optimized morpheme ranking. This technique gives slightly better accuracy while being conceptually simpler and more efficient. The approach, after further enhancements, was evaluated on a version of the Quranic Arabic Corpus, attaining a final accuracy of approximately 93%. A comparative evaluation shows this to be superior to two existing, well used manually built Arabic stemmers, thus demonstrating the practical feasibility of unsupervised learning of non-concatenative morphology

Multimodal and disentangled representation learning for medical image analysis

Automated medical image analysis is a growing research field with various applications in modern healthcare. Furthermore, a multitude of imaging techniques (or modalities) have been developed, such as Magnetic Resonance (MR) and Computed Tomography (CT), to attenuate different organ characteristics. Research on image analysis is predominately driven by deep learning methods due to their demonstrated performance. In this thesis, we argue that their success and generalisation relies on learning good latent representations. We propose methods for learning spatial representations that are suitable for medical image data, and can combine information coming from different modalities. Specifically, we aim to improve cardiac MR segmentation, a challenging task due to varied images and limited expert annotations, by considering complementary information present in (potentially unaligned) images of other modalities. In order to evaluate the benefit of multimodal learning, we initially consider a synthesis task on spatially aligned multimodal brain MR images. We propose a deep network of multiple encoders and decoders, which we demonstrate outperforms existing approaches. The encoders (one per input modality) map the multimodal images into modality invariant spatial feature maps. Common and unique information is combined into a fused representation, that is robust to missing modalities, and can be decoded into synthetic images of the target modalities. Different experimental settings demonstrate the benefit of multimodal over unimodal synthesis, although input and output image pairs are required for training. The need for paired images can be overcome with the cycle consistency principle, which we use in conjunction with adversarial training to transform images from one modality (e.g. MR) to images in another (e.g. CT). This is useful especially in cardiac datasets, where different spatial and temporal resolutions make image pairing difficult, if not impossible. Segmentation can also be considered as a form of image synthesis, if one modality consists of semantic maps. We consider the task of extracting segmentation masks for cardiac MR images, and aim to overcome the challenge of limited annotations, by taking into account unannanotated images which are commonly ignored. We achieve this by defining suitable latent spaces, which represent the underlying anatomies (spatial latent variable), as well as the imaging characteristics (non-spatial latent variable). Anatomical information is required for tasks such as segmentation and regression, whereas imaging information can capture variability in intensity characteristics for example due to different scanners. We propose two models that disentangle cardiac images at different levels: the first extracts the myocardium from the surrounding information, whereas the second fully separates the anatomical from the imaging characteristics. Experimental analysis confirms the utility of disentangled representations in semi-supervised segmentation, and in regression of cardiac indices, while maintaining robustness to intensity variations such as the ones induced by different modalities. Finally, our prior research is aggregated into one framework that encodes multimodal images into disentangled anatomical and imaging factors. Several challenges of multimodal cardiac imaging, such as input misalignments and the lack of expert annotations, are successfully handled in the shared anatomy space. Furthermore, we demonstrate that this approach can be used to combine complementary anatomical information for the purpose of multimodal segmentation. This can be achieved even when no annotations are provided for one of the modalities. This thesis creates new avenues for further research in the area of multimodal and disentangled learning with spatial representations, which we believe are key to more generalised deep learning solutions in healthcare