Current Advances on Non-Melanoma Skin Cancer

The book is devoted to the design, application and characterization of thin films and structures, with special emphasis on optical applications. It comprises ten papers—five featured and five regular—authored by scientists all over the world. Diverse materials are studied and their possible applications are demonstrated and discussed—transparent conductive coatings and structures from ZnO doped with Al and Ga and Ti-doped SnO2, polymers and nanosized zeolite thin films for optical sensing, TiO2 with linear and nonlinear optical properties, organic diamagnetic materials, broadband optical coatings, CrWN glass molding coatings, and silicon on insulator waveguides

Advanced Computational Methods for Oncological Image Analysis

[Cancer is the second most common cause of death worldwide and encompasses highly variable clinical and biological scenarios. Some of the current clinical challenges are (i) early diagnosis of the disease and (ii) precision medicine, which allows for treatments targeted to specific clinical cases. The ultimate goal is to optimize the clinical workflow by combining accurate diagnosis with the most suitable therapies. Toward this, large-scale machine learning research can define associations among clinical, imaging, and multi-omics studies, making it possible to provide reliable diagnostic and prognostic biomarkers for precision oncology. Such reliable computer-assisted methods (i.e., artificial intelligence) together with clinicians’ unique knowledge can be used to properly handle typical issues in evaluation/quantification procedures (i.e., operator dependence and time-consuming tasks). These technical advances can significantly improve result repeatability in disease diagnosis and guide toward appropriate cancer care. Indeed, the need to apply machine learning and computational intelligence techniques has steadily increased to effectively perform image processing operations—such as segmentation, co-registration, classification, and dimensionality reduction—and multi-omics data integration.

Meta-analytic approaches for summarising and comparing the accuracy of medical tests

Medical tests are essential for patient care. Evidence-based assessment of the relative accuracy of competing diagnostic tests informs clinical and policy decision making. This thesis addresses questions centred on assessing the reliability and transparency of evidence from systematic reviews and meta-analyses of comparative test accuracy, including validity of meta-analytic methods. Case studies were used to highlight key methodological issues, and provided rationale and context for the thesis. Published systematic reviews of multiple tests were identified and used to provide a descriptive survey of recent practice. Availability of comparative accuracy studies and differences between meta-analyses of direct (head-to-head) and indirect (between-study) comparisons were assessed. Comparative meta-analysis methods were reviewed and those deemed statistically robust were empirically evaluated. Using simulation, performance of hierarchical methods for meta-analysis of a single test was investigated in challenging scenarios (e.g. few studies or sparse data) and implications for test comparisons were considered. Poor statistical methods and incomplete reporting threatens the reliability of comparative reviews. Differences exist between direct and indirect comparisons but direct comparisons were seldom feasible because comparative studies were unavailable. Furthermore, inappropriate use of meta-analytic methods generated misleading results and conclusions. Therefore, recommendations for use of valid methods and a reporting checklist were developed

Development of medical image/video segmentation via deep learning models

Image segmentation has a critical role in medical diagnosis systems as it is mostly the initial stage, and any error would be propagated in the subsequent analysis. Certain challenges, including Irregular border, low quality of images, small Region of Interest (RoI) and complex structures such as overlapping cells in images impede the improvement of medical image analysis. Deep learning-based algorithms have recently brought superior achievements in computer vision. However, there are limitations to their application in the medical domain including data scarcity, and lack of pretrained models on medical data. This research addresses the issues that hinder the progress of deep learning methods on medical data. Firstly, the effectiveness of transfer learning from a pretrained model with dissimilar data is investigated. The model is improved by integrating feature maps from the frequency domain into the spatial feature maps of Convolutional Neural Network (CNN). Training from scratch and the challenges ahead were explored as well. The proposed model shows higher performance compared to state-of-the-art methods by %2:2 and %17 in Jaccard index for tasks of lesion segmentation and dermoscopic feature segmentation respectively. Furthermore, the proposed model benefits from significant improvement for noisy images without preprocessing stage. Early stopping and drop out layers were considered to tackle the overfitting and network hyper-parameters such as different learning rate, weight initialization, kernel size, stride and normalization techniques were investigated to enhance learning performance. In order to expand the research into video segmentation, specifically left ventricular segmentation, U-net deep architecture was modified. The small RoI and confusion between overlapped organs are big challenges in MRI segmentation. The consistent motion of LV and the continuity of neighbor frames are important features that were used in the proposed architecture. High level features including optical flow and contourlet were used to add temporal information and the RoI module to the Unet model. The proposed model surpassed the results of original Unet model for LV segmentation by a %7 increment in Jaccard index

Fuzzy Logic

Fuzzy Logic is becoming an essential method of solving problems in all domains. It gives tremendous impact on the design of autonomous intelligent systems. The purpose of this book is to introduce Hybrid Algorithms, Techniques, and Implementations of Fuzzy Logic. The book consists of thirteen chapters highlighting models and principles of fuzzy logic and issues on its techniques and implementations. The intended readers of this book are engineers, researchers, and graduate students interested in fuzzy logic systems

When the machine does not know measuring uncertainty in deep learning models of medical images

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonRecently, Deep learning (DL), which involves powerful black box predictors, has outperformed human experts in several medical diagnostic problems. However, these methods focus exclusively on improving the accuracy of point predictions without assessing their outputs’ quality and ignore the asymmetric cost involved in different types of misclassification errors. Neural networks also do not deliver confidence in predictions and suffer from over and under confidence, i.e. are not well calibrated. Knowing how much confidence there is in a prediction is essential for gaining clinicians’ trust in the technology. Calibrated uncertainty quantification is a challenging problem as no ground truth is available. To address this, we make two observations: (i) cost-sensitive deep neural networks with Dropweights models better quantify calibrated predictive uncertainty, and (ii) estimated uncertainty with point predictions in Deep Ensembles Bayesian Neural Networks with DropWeights can lead to a more informed decision and improve prediction quality. This dissertation focuses on quantifying uncertainty using concepts from cost-sensitive neural networks, calibration of confidence, and Dropweights ensemble method. First, we show how to improve predictive uncertainty by deep ensembles of neural networks with Dropweights learning an approximate distribution over its weights in medical image segmentation and its application in active learning. Second, we use the Jackknife resampling technique to correct bias in quantified uncertainty in image classification and propose metrics to measure uncertainty performance. The third part of the thesis is motivated by the discrepancy between the model predictive error and the objective in quantified uncertainty when costs for misclassification errors or unbalanced datasets are asymmetric. We develop cost-sensitive modifications of the neural networks in disease detection and propose metrics to measure the quality of quantified uncertainty. Finally, we leverage an adaptive binning strategy to measure uncertainty calibration error that directly corresponds to estimated uncertainty performance and address problematic evaluation methods. We evaluate the effectiveness of the tools on nuclei images segmentation, multi-class Brain MRI image classification, multi-level cell type-specific protein expression prediction in ImmunoHistoChemistry (IHC) images and cost-sensitive classification for Covid-19 detection from X-Rays and CT image dataset. Our approach is thoroughly validated by measuring the quality of uncertainty. It produces an equally good or better result and paves the way for the future that addresses the practical problems at the intersection of deep learning and Bayesian decision theory. In conclusion, our study highlights the opportunities and challenges of the application of estimated uncertainty in deep learning models of medical images, representing the confidence of the model’s prediction, and the uncertainty quality metrics show a significant improvement when using Deep Ensembles Bayesian Neural Networks with DropWeights

Deep Learning in Medical Image Analysis

The accelerating power of deep learning in diagnosing diseases will empower physicians and speed up decision making in clinical environments. Applications of modern medical instruments and digitalization of medical care have generated enormous amounts of medical images in recent years. In this big data arena, new deep learning methods and computational models for efficient data processing, analysis, and modeling of the generated data are crucially important for clinical applications and understanding the underlying biological process. This book presents and highlights novel algorithms, architectures, techniques, and applications of deep learning for medical image analysis

Image Processing and Analysis for Preclinical and Clinical Applications

Radiomics is one of the most successful branches of research in the field of image processing and analysis, as it provides valuable quantitative information for the personalized medicine. It has the potential to discover features of the disease that cannot be appreciated with the naked eye in both preclinical and clinical studies. In general, all quantitative approaches based on biomedical images, such as positron emission tomography (PET), computed tomography (CT) and magnetic resonance imaging (MRI), have a positive clinical impact in the detection of biological processes and diseases as well as in predicting response to treatment. This Special Issue, “Image Processing and Analysis for Preclinical and Clinical Applications”, addresses some gaps in this field to improve the quality of research in the clinical and preclinical environment. It consists of fourteen peer-reviewed papers covering a range of topics and applications related to biomedical image processing and analysis