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The Combination of Data-Driven Machine Learning Approaches and Prior Knowledge for Robust Medical Image Processing and Analysis
With the availability of big image datasets and state-of-the-art computing hardware, data-driven machine learning approaches, particularly deep learning, have been used in numerous medical image (CT-scans, MRI, PET, SPECT, etc..) computing tasks, ranging from image reconstruction, super-resolution, segmentation, registration all the way to disease classification and survival prediction. However, training such high-precision approaches often require large amounts of data to be collected and labelled and high-capacity graphics processing units (GPUs) installed, which are resource intensive and hence not always practical. Other hurdles such as the generalization ability to unseen new data and difficulty to interpret and explain can prevent their deployment to those clinical applications which deem such abilities imperative.
Medical Image Understanding and Analysis
This book constitutes the refereed proceedings of the 23rd Conference on Medical Image Understanding and Analysis, MIUA 2019, held in Liverpool, UK, in July 2019. The 43 full papers presented were carefully reviewed and selected from 70 submissions. There were organized in topical sections named: oncology and tumour imaging; lesion, wound and ulcer analysis; biostatistics; fetal imaging; enhancement and reconstruction; diagnosis, classification and treatment; vessel and nerve analysis; image registration; image segmentation; ophthalmic imaging; and posters.
Medical Image Understanding and Analysis
This book constitutes the refereed proceedings of the 21st Annual Conference on Medical Image Understanding and Analysis, MIUA 2017, held in Edinburgh, UK, in July 2017. The 82 revised full papers presented were carefully reviewed and selected from 105 submissions. The papers are organized in topical sections on retinal imaging, ultrasound imaging, cardiovascular imaging, oncology imaging, mammography image analysis, image enhancement and alignment, modeling and segmentation of preclinical, body and histological imaging, feature detection and classification. The chapters 'Model-Based Correction of Segmentation Errors in Digitised Histological Images' and 'Unsupervised Superpixel-Based Segmentation of Histopathological Images with Consensus Clustering' are open access under a CC BY 4.0 license.
Mathematics Rebooted
Would you like to understand more mathematics? Many people would. Perhaps at school you liked mathematics for a while but were then put off because you missed a key idea and kept getting stuck. Perhaps you always liked mathematics but gave it up because your main interest was music or languages or science or philosophy. Or perhaps you studied mathematics to advanced levels, but have now forgotten most of what you once knew. Whichever is the case, this book is for you. It aims to build on what you know, revisiting basic ideas with a focus on meaning. Each chapter starts with an idea from school mathematics - often primary school mathematics - and gradually builds up a network of links to more...
Medical Image Understanding and Analysis
This book constitutes the refereed proceedings of the 22st Annual Conference on Medical Image Understanding and Analysis, MIUA 2018, held in Southampton, UK, in July 2018.The 34 revised full papers presented were carefully reviewed and selected from 49 submissions. The papers are organized in topical sections on liver analysis, medical image analysis, texture and image analysis, MRI: applications and techniques, segmentation in medical images, CT: learning and planning, ocular imaging analysis, applications of medical image analysis.
Riemannian Geometric Statistics in Medical Image Analysis
Over the past 15 years, there has been a growing need in the medical image computing community for principled methods to process nonlinear geometric data. Riemannian geometry has emerged as one of the most powerful mathematical and computational frameworks for analyzing such data. Riemannian Geometric Statistics in Medical Image Analysis is a complete reference on statistics on Riemannian manifolds and more general nonlinear spaces with applications in medical image analysis. It provides an introduction to the core methodology followed by a presentation of state-of-the-art methods. Content includes: - The foundations of Riemannian geometric methods for statistics on manifolds with emphasis o...
Medical Image Understanding and Analysis
This book constitutes the refereed proceedings of the 26th Conference on Medical Image Understanding and Analysis, MIUA 2022, held in Cambridge, UK, in July 2022. The 65 full papers presented were carefully reviewed and selected from 95 submissions. They were organized according to following topical sections: biomarker detection; image registration, and reconstruction; image segmentation; generative models, biomedical simulation and modelling; classification; image enhancement, quality assessment, and data privacy; radiomics, predictive models, and quantitative imaging. Chapter “FCN-Transformer Feature Fusion for Polyp Segmentation” is available open access under a Creative Commons Attribution 4.0 International License via link.springer.com.
Advances in Complex Data Modeling and Computational Methods in Statistics
The book is addressed to statisticians working at the forefront of the statistical analysis of complex and high dimensional data and offers a wide variety of statistical models, computer intensive methods and applications: network inference from the analysis of high dimensional data; new developments for bootstrapping complex data; regression analysis for measuring the downsize reputational risk; statistical methods for research on the human genome dynamics; inference in non-euclidean settings and for shape data; Bayesian methods for reliability and the analysis of complex data; methodological issues in using administrative data for clinical and epidemiological research; regression models with differential regularization; geostatistical methods for mobility analysis through mobile phone data exploration. This volume is the result of a careful selection among the contributions presented at the conference "S.Co.2013: Complex data modeling and computationally intensive methods for estimation and prediction" held at the Politecnico di Milano, 2013. All the papers published here have been rigorously peer-reviewed.
Recentering the World
Recentering the World recovers a richly contextual, detailed history of Western-imposed legal structures in China, as well as engagements with international law by Chinese officials, jurists, and citizens. Beginning in the Late Qing era, it shows how international law functioned as a channel for power relations, techniques of economic domination, as well as novel forms of resistance. The book also radically diversifies traditionally Eurocentric accounts of modern international law's origins, demonstrating how, by the mid-twentieth century, Chinese jurists had made major contributions to international organizations and the UN system, the international judiciary, the laws of armed conflict, and more. Drawing on extensive archival research, this book is a valuable guide to China's often conflicted role in international law, its reception and contention of concepts of sovereignty, property, obligation, and autonomy, and its gradual move from the 'periphery' to a shared spot at the 'center' of global legal order.
