Improving sparse recovery on structured images with bagged clustering

International audience—The identification of image regions associated with external variables through discriminative approaches yields ill-posed estimation problems. This estimation challenge can be tackled by imposing sparse solutions. However, the sensitivity of sparse estimators to correlated variables leads to non-reproducible results, and only a subset of the important variables are selected. In this paper, we explore an approach based on bagging clustering-based data compression in order to alleviate the instability of sparse models. Specifically, we design a new framework in which the estimator is built by averaging multiple models estimated after feature clustering, to improve the conditioning of the model. We show that this combination of model averaging with spatially consistent compression can have the virtuous effect of increasing the stability of the weight maps, allowing a better interpretation of the results. Finally, we demonstrate the benefit of our approach on several predictive modeling problems

Nephroblastoma in MRI Data

The main objective of this work is the mathematical analysis of nephroblastoma in MRI sequences. At the beginning we provide two different datasets for segmentation and classification. Based on the first dataset, we analyze the current clinical practice regarding therapy planning on the basis of annotations of a single radiologist. We can show with our benchmark that this approach is not optimal and that there may be significant differences between human annotators and even radiologists. In addition, we demonstrate that the approximation of the tumor shape currently used is too coarse granular and thus prone to errors. We address this problem and develop a method for interactive segmentation that allows an intuitive and accurate annotation of the tumor. While the first part of this thesis is mainly concerned with the segmentation of Wilms’ tumors, the second part deals with the reliability of diagnosis and the planning of the course of therapy. The second data set we compiled allows us to develop a method that dramatically improves the differential diagnosis between nephroblastoma and its precursor lesion nephroblastomatosis. Finally, we can show that even the standard MRI modality for Wilms’ tumors is sufficient to estimate the developmental tendencies of nephroblastoma under chemotherapy

An image processing decisional system for the Achilles tendon using ultrasound images

The Achilles Tendon (AT) is described as the largest and strongest tendon in the human body. As for any other organs in the human body, the AT is associated with some medical problems that include Achilles rupture and Achilles tendonitis. AT rupture affects about 1 in 5,000 people worldwide. Additionally, AT is seen in about 10 percent of the patients involved in sports activities. Today, ultrasound imaging plays a crucial role in medical imaging technologies. It is portable, non-invasive, free of radiation risks, relatively inexpensive and capable of taking real-time images. There is a lack of research that looks into the early detection and diagnosis of AT abnormalities from ultrasound images. This motivated the researcher to build a complete system which enables one to crop, denoise, enhance, extract the important features and classify AT ultrasound images. The proposed application focuses on developing an automated system platform. Generally, systems for analysing ultrasound images involve four stages, pre-processing, segmentation, feature extraction and classification. To produce the best results for classifying the AT, SRAD, CLAHE, GLCM, GLRLM, KPCA algorithms have been used. This was followed by the use of different standard and ensemble classifiers trained and tested using the dataset samples and reduced features to categorize the AT images into normal or abnormal. Various classifiers have been adopted in this research to improve the classification accuracy. To build an image decisional system, a 57 AT ultrasound images has been collected. These images were used in three different approaches where the Region of Interest (ROI) position and size are located differently. To avoid the imbalanced misleading metrics, different evaluation metrics have been adapted to compare different classifiers and evaluate the whole classification accuracy. The classification outcomes are evaluated using different metrics in order to estimate the decisional system performance. A high accuracy of 83% was achieved during the classification process. Most of the ensemble classifies worked better than the standard classifiers in all the three ROI approaches. The research aim was achieved and accomplished by building an image processing decisional system for the AT ultrasound images. This system can distinguish between normal and abnormal AT ultrasound images. In this decisional system, AT images were improved and enhanced to achieve a high accuracy of classification without any user intervention

AI-big data analytics for building automation and management systems: a survey, actual challenges and future perspectives

In theory, building automation and management systems (BAMSs) can provide all the components and functionalities required for analyzing and operating buildings. However, in reality, these systems can only ensure the control of heating ventilation and air conditioning system systems. Therefore, many other tasks are left to the operator, e.g. evaluating buildings’ performance, detecting abnormal energy consumption, identifying the changes needed to improve efficiency, ensuring the security and privacy of end-users, etc. To that end, there has been a movement for developing artificial intelligence (AI) big data analytic tools as they offer various new and tailor-made solutions that are incredibly appropriate for practical buildings’ management. Typically, they can help the operator in (i) analyzing the tons of connected equipment data; and; (ii) making intelligent, efficient, and on-time decisions to improve the buildings’ performance. This paper presents a comprehensive systematic survey on using AI-big data analytics in BAMSs. It covers various AI-based tasks, e.g. load forecasting, water management, indoor environmental quality monitoring, occupancy detection, etc. The first part of this paper adopts a well-designed taxonomy to overview existing frameworks. A comprehensive review is conducted about different aspects, including the learning process, building environment, computing platforms, and application scenario. Moving on, a critical discussion is performed to identify current challenges. The second part aims at providing the reader with insights into the real-world application of AI-big data analytics. Thus, three case studies that demonstrate the use of AI-big data analytics in BAMSs are presented, focusing on energy anomaly detection in residential and office buildings and energy and performance optimization in sports facilities. Lastly, future directions and valuable recommendations are identified to improve the performance and reliability of BAMSs in intelligent buildings

Advances in electron microscopy with deep learning

Following decades of exponential increases in computational capability and widespread data availability, deep learning is readily enabling new science and technology. This thesis starts with a review of deep learning in electron microscopy, which offers a practical perspective aimed at developers with limited familiarity. To help electron microscopists get started with started with deep learning, large new electron microscopy datasets are introduced for machine learning. Further, new approaches to variational autoencoding are introduced to embed datasets in low-dimensional latent spaces, which are used as the basis of electron microscopy search engines. Encodings are also used to investigate electron microscopy data visualization by t-distributed stochastic neighbour embedding. Neural networks that process large electron microscopy images may need to be trained with small batch sizes to fit them into computer memory. Consequently, adaptive learning rate clipping is introduced to prevent learning being destabilized by loss spikes associated with small batch sizes. This thesis presents three applications of deep learning to electron microscopy. Firstly, electron beam exposure can damage some specimens, so generative adversarial networks were developed to complete realistic images from sparse spiral, gridlike, and uniformly spaced scans. Further, recurrent neural networks were trained by reinforcement learning to dynamically adapt sparse scans to specimens. Sparse scans can decrease electron beam exposure and scan time by 10-100× with minimal information loss. Secondly, a large encoder-decoder was developed to improve transmission electron micrograph signal-to-noise. Thirdly, conditional generative adversarial networks were developed to recover exit wavefunction phases from single images. Phase recovery with deep learning overcomes existing limitations as it is suitable for live applications and does not require microscope modification. To encourage further investigation, scientific publications and their source files, source code, pretrained models, datasets, and other research outputs covered by this thesis are openly accessible

Market Segmentation Analysis

Business; Management science; Market research; Statistics

Market Segmentation Analysis: Understanding It, Doing It, and Making It Useful

This open access book offers something for everyone working with market segmentation: practical guidance for users of market segmentation solutions; organisational guidance on implementation issues; guidance for market researchers in charge of collecting suitable data; and guidance for data analysts with respect to the technical and statistical aspects of market segmentation analysis. Even market segmentation experts will find something new, including a vast array of useful visualisation techniques that make interpretation of market segments and selection of target segments easier. All calculations are accompanied not only with a detailed explanation, but also with R code that allows readers to replicate any aspect of what is being covered in the book using R, the open-source environment for statistical computing and graphics.Dieses Open Access Buch offeriert allen etwas, die mit Marktsegmentierung zu tun haben: praktische Anleitungen für Anwender von Marktsegmentierungslösungen, organisatorische Hilfe zur Umsetzung und Datensammlung, sowie Hilfe zur technischen und statistischen Umsetzung von Marktsegmentierungsanalysen. Auch Experten der Marktsegmentierung finden neue Werkzeuge, inbesonders eine umfangreiche Sammlung von Visualisierungsmethoden zur einfacheren Interpretation und Selektion von Marktsegmenten. Alle Berechnungen werden nicht nur detailliert erklärt, sondern von R Code begleitet, welcher es dem Leser erlaubt, alle Analysen im Buch mit Hilfe der Open Source Statistiksoftware R zu replizieren