Towards robust machine learning with graph neural networks

In order to apply Neural Networks in safety-critical settings, such as healthcare or autonomous driving, we need to be able to analyse their robustness against adversarial attacks. These attacks perturb natural images by adding small, carefully chosen perturbations to them that are imperceptible to the human eye. Trained neural networks with high training and validation accuracy often misclassify a large number of these perturbed images. In this thesis we propose several new methods aimed at analysing the robustness of trained neural networks to adversarial attacks. In the first part, we improve upon existing methods to generate adversarial examples more efficiently. We note that past work in this field has relied on optimization methods that ignore the inherent structure of the problem and data, or generative methods that rely purely on learning and often fail to generate adversarial examples where they are hard to find. To alleviate these deficiencies, we propose a novel stand-alone attack based on a GNN that takes advantage of the strengths of both approaches. Our GNN computes descent directions to guide an iterative procedure towards adversarial examples. Our next contribution is inspired by the observation that many state-of-the-art adversarial attacks require many random restarts to generate adversarial examples. Each time we perform a restart we ignore all previous unsuccessful runs. In order to alleviate this deficiency, we propose a method that learns from its mistakes. Specifically, our method uses GNNs as an attention, to greatly reduce the search space for future iterations of the attacks. For our final contribution, we note that adversarial attacks may fail, even where adversarial examples exist. We thus focus on formal complete neural network verification which returns a sound and complete proof of robustness. Recent years have witnessed the deployment of branch-and-bound (BaB) frameworks for formal verification in deep learning. The main computational bottleneck of BaB is the estimation of lower bounds. Past work in this field has relied on traditional optimization algorithms whose inefficiencies have limited their scope. To alleviate this deficiency, we propose a novel graph neural network (GNN) based approach. Our GNN aims to compute a dual solution of the convex relaxation, thereby providing a valid lower bound, which, if positive, proves robustness

CD or not CD, that is the question - a digital interobserver agreement study in coeliac disease

OBJECTIVE: Coeliac disease (CD) diagnosis generally depends on histological examination of duodenal biopsies. We present the first study analysing the concordance in examination of duodenal biopsies using digitised whole-slide images (WSIs). We further investigate whether the inclusion of IgA tTG and haemoglobin (Hb) data improves the inter-observer agreement of diagnosis.DESIGN: We undertook a large study of the concordance in histological examination of duodenal biopsies using digitised WSIs in an entirely virtual reporting setting. Our study was organised in two phases: in phase one, 13 pathologists independently classified 100 duodenal biopsies (40 normal; 40 CD; 20 indeterminate enteropathy) in the absence of any clinical or laboratory data. In phase two, the same pathologists examined the (re-anonymised) WSIs with the inclusion of IgA tTG and Hb data.RESULTS: We found the mean probability of two observers agreeing in the absence of additional data to be 0.73 (±0.08) with a corresponding Cohen's kappa of 0.59 (±0.11). We further showed that the inclusion of additional data increased the concordance to 0.80 (±0.06) with a Cohen's kappa coefficient of 0.67 (±0.09).CONCLUSION: We showed that the addition of serological data significantly improves the quality of CD diagnosis. However, the limited inter-observer agreement in CD diagnosis using digitised WSIs, even after the inclusion of IgA tTG and Hb data, indicates the important of interpreting duodenal biopsy in the appropriate clinical context. It further highlights the unmet need for an objective means of reproducible duodenal biopsy diagnosis, such as the automated analysis of WSIs using AI.<br/

Some challenges in the first-principles modeling of structures and processes in electrochemical energy storage and transfer

In spite of the strong relevance of electrochemical energy conversion and storage, the atomistic modeling of structures and processes in electrochemical systems from first principles is hampered by severe problems. Among others, these problems are associated with the theoretical description of the electrode potential, the characterization of interfaces, the proper treatment of liquid electrolytes, changes in the bulk structure of battery electrodes, and limitations of the functionals used in first-principles electronic structure calculations. We will illustrate these obstacles, but also indicate strategies to overcome them

CD, or not CD, that is the question - a digital inter-observer agreement study in coeliac disease

OBJECTIVE: Coeliac disease (CD) diagnosis generally depends on histological examination of duodenal biopsies. We present the first study analysing the concordance in examination of duodenal biopsies using digitised whole-slide images (WSIs). We further investigate whether the inclusion of IgA tTG and haemoglobin (Hb) data improves the inter-observer agreement of diagnosis. DESIGN: We undertook a large study of the concordance in histological examination of duodenal biopsies using digitised WSIs in an entirely virtual reporting setting. Our study was organised in two phases: in phase one, 13 pathologists independently classified 100 duodenal biopsies (40 normal; 40 CD; 20 indeterminate enteropathy) in the absence of any clinical or laboratory data. In phase two, the same pathologists examined the (re-anonymised) WSIs with the inclusion of IgA tTG and Hb data. RESULTS: We found the mean probability of two observers agreeing in the absence of additional data to be 0.73 (±0.08) with a corresponding Cohen's kappa of 0.59 (±0.11). We further showed that the inclusion of additional data increased the concordance to 0.80 (±0.06) with a Cohen's kappa coefficient of 0.67 (±0.09). CONCLUSION: We showed that the addition of serological data significantly improves the quality of CD diagnosis. However, the limited inter-observer agreement in CD diagnosis using digitised WSIs, even after the inclusion of IgA tTG and Hb data, indicates the important of interpreting duodenal biopsy in the appropriate clinical context. It further highlights the unmet need for an objective means of reproducible duodenal biopsy diagnosis, such as the automated analysis of WSIs using AI

CD, or not CD, that is the question: a digital interobserver agreement study in coeliac disease.

Peer reviewed: TrueAcknowledgements: JD, MJA and ES acknowledge Graham Snudden for organisational support. JD, BS, FJ, MJA and ES are grateful to the 17 GI pathology experts for agreeing to participate, and to MW for facilitating virtual access to the WSIs, all of whom made this work possible.OBJECTIVE: Coeliac disease (CD) diagnosis generally depends on histological examination of duodenal biopsies. We present the first study analysing the concordance in examination of duodenal biopsies using digitised whole-slide images (WSIs). We further investigate whether the inclusion of immunoglobulin A tissue transglutaminase (IgA tTG) and haemoglobin (Hb) data improves the interobserver agreement of diagnosis. DESIGN: We undertook a large study of the concordance in histological examination of duodenal biopsies using digitised WSIs in an entirely virtual reporting setting. Our study was organised in two phases: in phase 1, 13 pathologists independently classified 100 duodenal biopsies (40 normal; 40 CD; 20 indeterminate enteropathy) in the absence of any clinical or laboratory data. In phase 2, the same pathologists examined the (re-anonymised) WSIs with the inclusion of IgA tTG and Hb data. RESULTS: We found the mean probability of two observers agreeing in the absence of additional data to be 0.73 (±0.08) with a corresponding Cohen's kappa of 0.59 (±0.11). We further showed that the inclusion of additional data increased the concordance to 0.80 (±0.06) with a Cohen's kappa coefficient of 0.67 (±0.09). CONCLUSION: We showed that the addition of serological data significantly improves the quality of CD diagnosis. However, the limited interobserver agreement in CD diagnosis using digitised WSIs, even after the inclusion of IgA tTG and Hb data, indicates the importance of interpreting duodenal biopsy in the appropriate clinical context. It further highlights the unmet need for an objective means of reproducible duodenal biopsy diagnosis, such as the automated analysis of WSIs using artificial intelligence

CD, or not CD, that is the question:a digital interobserver agreement study in coeliac disease

OBJECTIVE: Coeliac disease (CD) diagnosis generally depends on histological examination of duodenal biopsies. We present the first study analysing the concordance in examination of duodenal biopsies using digitised whole-slide images (WSIs). We further investigate whether the inclusion of immunoglobulin A tissue transglutaminase (IgA tTG) and haemoglobin (Hb) data improves the interobserver agreement of diagnosis.DESIGN: We undertook a large study of the concordance in histological examination of duodenal biopsies using digitised WSIs in an entirely virtual reporting setting. Our study was organised in two phases: in phase 1, 13 pathologists independently classified 100 duodenal biopsies (40 normal; 40 CD; 20 indeterminate enteropathy) in the absence of any clinical or laboratory data. In phase 2, the same pathologists examined the (re-anonymised) WSIs with the inclusion of IgA tTG and Hb data.RESULTS: We found the mean probability of two observers agreeing in the absence of additional data to be 0.73 (±0.08) with a corresponding Cohen's kappa of 0.59 (±0.11). We further showed that the inclusion of additional data increased the concordance to 0.80 (±0.06) with a Cohen's kappa coefficient of 0.67 (±0.09).CONCLUSION: We showed that the addition of serological data significantly improves the quality of CD diagnosis. However, the limited interobserver agreement in CD diagnosis using digitised WSIs, even after the inclusion of IgA tTG and Hb data, indicates the importance of interpreting duodenal biopsy in the appropriate clinical context. It further highlights the unmet need for an objective means of reproducible duodenal biopsy diagnosis, such as the automated analysis of WSIs using artificial intelligence.</p