213 research outputs found
AI: Limits and Prospects of Artificial Intelligence
The emergence of artificial intelligence has triggered enthusiasm and promise of boundless opportunities as much as uncertainty about its limits. The contributions to this volume explore the limits of AI, describe the necessary conditions for its functionality, reveal its attendant technical and social problems, and present some existing and potential solutions. At the same time, the contributors highlight the societal and attending economic hopes and fears, utopias and dystopias that are associated with the current and future development of artificial intelligence
Generative Adversarial Network (GAN) for Medical Image Synthesis and Augmentation
Medical image processing aided by artificial intelligence (AI) and machine learning (ML) significantly improves medical diagnosis and decision making. However, the difficulty to access well-annotated medical images becomes one of the main constraints on further improving this technology.
Generative adversarial network (GAN) is a DNN framework for data synthetization, which provides a practical solution for medical image augmentation and translation. In this study, we first perform a quantitative survey on the published studies on GAN for medical image processing since 2017. Then a novel adaptive cycle-consistent adversarial network (Ad CycleGAN) is proposed. We respectively use a malaria blood cell dataset (19,578 images) and a COVID-19 chest X-ray dataset (2,347 images) to test the new Ad CycleGAN. The quantitative metrics include mean squared error (MSE), root mean squared error (RMSE), peak signal-to-noise ratio (PSNR), universal image quality index (UIQI), spatial correlation coefficient (SCC), spectral angle mapper (SAM), visual information fidelity (VIF), Frechet inception distance (FID), and the classification accuracy of the synthetic images. The CycleGAN and variant autoencoder (VAE) are also implemented and evaluated as comparison.
The experiment results on malaria blood cell images indicate that the Ad CycleGAN generates more valid images compared to CycleGAN or VAE. The synthetic images by Ad CycleGAN or CycleGAN have better quality than those by VAE. The synthetic images by Ad CycleGAN have the highest accuracy of 99.61%. In the experiment on COVID-19 chest X-ray, the synthetic images by Ad CycleGAN or CycleGAN have higher quality than those generated by variant autoencoder (VAE). However, the synthetic images generated through the homogenous image augmentation process have better quality than those synthesized through the image translation process. The synthetic images by Ad CycleGAN have higher accuracy of 95.31% compared to the accuracy of the images by CycleGAN of 93.75%.
In conclusion, the proposed Ad CycleGAN provides a new path to synthesize medical images with desired diagnostic or pathological patterns. It is considered a new approach of conditional GAN with effective control power upon the synthetic image domain. The findings offer a new path to improve the deep neural network performance in medical image processing
Data Management for Dynamic Multimedia Analytics and Retrieval
Multimedia data in its various manifestations poses a unique challenge from a data storage and data management perspective, especially if search, analysis and analytics in large data corpora is considered. The inherently unstructured nature of the data itself and the curse of dimensionality that afflicts the representations we typically work with in its stead are cause for a broad range of issues that require sophisticated solutions at different levels. This has given rise to a huge corpus of research that puts focus on techniques that allow for effective and efficient multimedia search and exploration. Many of these contributions have led to an array of purpose-built, multimedia search systems.
However, recent progress in multimedia analytics and interactive multimedia retrieval, has demonstrated that several of the assumptions usually made for such multimedia search workloads do not hold once a session has a human user in the loop. Firstly, many of the required query operations cannot be expressed by mere similarity search and since the concrete requirement cannot always be anticipated, one needs a flexible and adaptable data management and query framework. Secondly, the widespread notion of staticity of data collections does not hold if one considers analytics workloads, whose purpose is to produce and store new insights and information. And finally, it is impossible even for an expert user to specify exactly how a data management system should produce and arrive at the desired outcomes of the potentially many different queries.
Guided by these shortcomings and motivated by the fact that similar questions have once been answered for structured data in classical database research, this Thesis presents three contributions that seek to mitigate the aforementioned issues. We present a query model that generalises the notion of proximity-based query operations and formalises the connection between those queries and high-dimensional indexing. We complement this by a cost-model that makes the often implicit trade-off between query execution speed and results quality transparent to the system and the user. And we describe a model for the transactional and durable maintenance of high-dimensional index structures.
All contributions are implemented in the open-source multimedia database system Cottontail DB, on top of which we present an evaluation that demonstrates the effectiveness of the proposed models. We conclude by discussing avenues for future research in the quest for converging the fields of databases on the one hand and (interactive) multimedia retrieval and analytics on the other
Mathematical modeling of Lynch syndrome carcinogenesis
Cancer is one of the leading causes of disease-related death worldwide. In recent years, large amounts of data on cancer genetics and molecular characteristics have become available and accumulated with increasing speed. However, the current understanding of cancer as a disease is still limited by the lack of suitable models that allow interpreting these data in proper ways. Thus, the highly interdisciplinary research field of mathematical oncology has evolved to use mathematics, modeling, and simulations to study cancer with the overall goal to improve clinical patient care.
This dissertation aims at developing mathematical models and tools for different spatial scales of cancer development at the example of colorectal cancer in Lynch syndrome, the most common inherited colorectal cancer predisposition syndrome. We derive model-driven approaches for carcinogenesis at the DNA, cell, and crypt level, as well as data-driven methods for cancer-immune interactions at the DNA level and for the evaluation of diagnostic procedures at the Lynch syndrome population level. The developed models present an important step toward an improved understanding of hereditary cancer as a disease aiming at rapid implementation into clinical management guidelines and into the development of novel, innovative approaches for prevention and treatment
A Self-Supervised Contrastive Learning Approach for Whole Slide Image Representation in Digital Pathology
Digital pathology has recently expanded the field of medical image processing for di- agnostic reasons. Whole slide images (WSIs) of histopathology are often accompanied by information on the location and type of diseases and cancers displayed. Digital scanning has made it possible to create high-quality WSIs from tissue slides quickly. As a result, hospitals and clinics now have more WSI archives. As a result, rapid WSI analysis is nec- essary to meet the demands of modern pathology workflow. The advantages of pathology have increased the popularity of computerized image analysis and diagnosis.
The recent development of artificial neural networks in AI has changed the field of digital pathology. Deep learning can help pathologists segment and categorize regions and nuclei and search among WSIs for comparable morphology. However, because of the large data size of WSIs, representing digitized pathology slides has proven difficult. Furthermore, the morphological differences between diagnoses may be slim, making WSI representation problematic. Convolutional neural networks are currently being used to generate a single vector representation from a WSI (CNN). Multiple instance learning is a solution to tackle the problem of giga-pixel image representation. In multiple instance learning, all patches in a slide are combined to create a single vector representation.
Self-supervised learning has also shown impressive generalization outcomes in recent years. In self-supervised learning, a model is trained using pseudo-labels on a pretext task to improve accuracy on the main goal task. Contrastive learning is also a new scheme for self-supervision that aids the model produce more robust presentations. In this thesis, we describe a self-supervised approach that utilizes the anatomic site information provided by each WSI during tissue preparation and digitization. We exploit an Attention-based Multiple instance learning setup along with supervised contrastive learning. Furthermore, we show that using supervised contrastive learning approaches in the pretext stage improves model embedding quality in both classification and search tasks. We test our model on an image search on the TCGA depository dataset, a Lung cancer classification task and a Lung-Kidney-Stomach immunofluorescence WSI dataset
The extent of Kuwaiti Islamic banks restrict the use of Islamic financing tools in their financial operations: a field study
This research aims to identify the extent to of Kuwaiti Islamic banks adhere to the use of Islamic financing tools in their financial operations. The study population consists of all (5) banks listed on the Kuwait Stock Exchange. As for the study
sample, (100) respondents were selected from Financial managers, accountants, and workers in finance and investment departments work in these banks. The questionnaire was used as a tool for collecting primary data. The results showed that Kuwaiti Islamic banks adhere to the use of Islamic financing tools represented in Murabaha,
Musharaka and Mudaraba in their financial operations to a high degree. The study recommended that Kuwaiti Islamic banks should be encouraged to play a more role in Murabaha operations and find appropriate solutions to technical obstacles and culture-related procedures that prevent the provision of Islamic financing through Murabaha
Entropy in Image Analysis III
Image analysis can be applied to rich and assorted scenarios; therefore, the aim of this recent research field is not only to mimic the human vision system. Image analysis is the main methods that computers are using today, and there is body of knowledge that they will be able to manage in a totally unsupervised manner in future, thanks to their artificial intelligence. The articles published in the book clearly show such a future
Multimedia Forensics
This book is open access. Media forensics has never been more relevant to societal life. Not only media content represents an ever-increasing share of the data traveling on the net and the preferred communications means for most users, it has also become integral part of most innovative applications in the digital information ecosystem that serves various sectors of society, from the entertainment, to journalism, to politics. Undoubtedly, the advances in deep learning and computational imaging contributed significantly to this outcome. The underlying technologies that drive this trend, however, also pose a profound challenge in establishing trust in what we see, hear, and read, and make media content the preferred target of malicious attacks. In this new threat landscape powered by innovative imaging technologies and sophisticated tools, based on autoencoders and generative adversarial networks, this book fills an important gap. It presents a comprehensive review of state-of-the-art forensics capabilities that relate to media attribution, integrity and authenticity verification, and counter forensics. Its content is developed to provide practitioners, researchers, photo and video enthusiasts, and students a holistic view of the field
ECLAP 2012 Conference on Information Technologies for Performing Arts, Media Access and Entertainment
It has been a long history of Information Technology innovations within the Cultural Heritage areas. The Performing arts has also been enforced with a number of new innovations which unveil a range of synergies and possibilities. Most of the technologies and innovations produced for digital libraries, media entertainment and education can be exploited in the field of performing arts, with adaptation and repurposing. Performing arts offer many interesting challenges and opportunities for research and innovations and exploitation of cutting edge research results from interdisciplinary areas. For these reasons, the ECLAP 2012 can be regarded as a continuation of past conferences such as AXMEDIS and WEDELMUSIC (both pressed by IEEE and FUP). ECLAP is an European Commission project to create a social network and media access service for performing arts institutions in Europe, to create the e-library of performing arts, exploiting innovative solutions coming from the ICT
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