Attention Mechanisms in the Classification of Histological Images

Recently, there has been an increase in the number of medical exams prescribed by medical doctors, not only to diagnose but also to keep track of the evolution of pathologies. In this sense, one of the medical specialties where the mentioned increase in the prescription rate has been observed is oncology. In this regard, not only to efficiently diagnose but also to monitor the evolution of the mentioned diseases, CT (Computed Tomography) scans, MRIs (Magnetic Resonance Imaging), and Biopsies are imaging techniques commonly used. After the exams are performed and the results retrieved by the respective health professionals, their analysis and interpretation are mandatory. This process, carried out by medical experts, is usually a time-consuming and tiring task. In this sense and to reduce the workload of these experts and support decision making, the research community start proposing several computer-aided systems, whose primary goal is to efficiently distinguish between healthy images and tumoral ones. Despite the success achieved by these methodologies, it become evident that the distinction of the two mentioned image categories (healthy and not-healthy) was associated with small regions of the images, and therefore not all image regions were equally important for diagnostic purposes. In this line of thinking, attention mechanisms start being considered to highlight important regions and neglect unimportant ones, leading to more correct predictions. In this thesis, we aim to study the impact of such mechanisms in the extraction of features from histopathological images of the epithelium from the oral cavity. In order to access the quality of the generated features for diagnostic purposes, those features were used to distinguish healthy from cancerous histopathological images.Recentemente, tem-se observado uma tendência crescente no número de exames médicos prescritos por médicos, no sentido de diagnosticar e acompanhar a evolução de patologias. Deste modo, uma das especialidades médicas onde a referida taxa de prescrição se assinala bastante elevada é a oncologia. No sentido de não só diagnosticar com eficácia, mas também para que a evolução das patologias seja devidamente seguida, é comum recorrer-se a técnicas de imagiologia como TACs (Tomografia Axial Computorizadas), RMs (Ressonâncias Magnéticas) ou Biópsias. Após a recepção dos respectivos exames médicos é necessário a sua análise e interpretação pelos profissionais competentes. Este processo é frequentemente moroso e cansativo para estes profissionais. No sentido de reduzir o labor destes profissionais e apoiar a tomada de decisão, começaram a surgir na literatura diversos sistemas computacionais cujo objectivo é distinguir imagens saudáveis de imagens não-saudáveis. Apesar do sucesso alcançado por estes sistemas, rapidamente se verificou que a distinção das duas classes de imagens é dependente de pequenas regiões, neste sentido nem todas as regiões constituintes da imagem são igualmente importantes para a distinção acima indicada. Posto isto, foram considerados mecanismos de atenção no sentido de maior importância dar a porções relevantes da imagem e negligenciar menos importantes, conduzindo a previsões mais correctas. Nesta dissertação pretende-se fazer um estudo do impacto destes mecanismos na extracção de features de imagens histopatológicas da mucosa oral. No sentido de avaliar a qualidade das features extraídas para o diagnóstico, estas são usadas por classificadores para a distinção de imagens saudáveis e cancerígenas

Interpreting Deep Learning for cell differentiation. Supervised and Unsupervised models viewed through the lens of information and perturbation theory.

"Predicting the future isn't magic, it's artificial intelligence" Dave Waters. In the last decades there has been an unprecedented growth in the field of machine learning, and particularly within deep learning models. The combination of big data and computational power has nurtured the evolution of a variety of new methods to predict and interpret future scenarios. These data centric models can achieve exceptional performances on specific tasks, with their prediction boundaries continuously expanding towards new and more complex challenges. However, the model complexity often translates into a lack of interpretability from a scientific c perspective, it is not trivial to identify the factors involved in final outcomes. Explainability may not always be a requirement for some machine learning tasks, specially when it comes in detriment of performance power. But for some applications, such as biological discoveries or medical diagnostics, understanding the output and determining factors that influence decisions is essential. In this thesis we develop both a supervised and unsupervised approach to map from genotype to phenotype. We emphasise the importance of interpretability and feature extraction from the models, by identifying relevant genes for cell differentiation. We then continue to explore the rules and mechanisms behind the models from a theoretical perspective. Using information theory to explain the learning process and applying perturbation theory to transform the results into a generalisable representation. We start by building a supervised approach to mapping cell profiles from genotype to phenotype, using single cell RNA-Seq data. We leverage non-linearities among gene expressions to identify cellular levels of differentiation. The ambiguity and even absence of labels in most biological studies instigated the development of novel unsupervised techniques, leading to a new general and biologically interpretable framework based on Variational Autoencoders. The application and validation of the methods has proven to be successful, but questions regarding the learning process and generative nature of the results remained unanswered. I use information theory to define a new approach to interpret training and the converged solutions of our models. The variational and generative nature of Autoencoders provides a platform to develop general models. Their results should extrapolate and allow generalisation beyond the boundaries of the observed data. To this extent, we introduce for the first time a new interpretation of the embedded generative functions through Perturbation Theory. The embedding multiplicity is addressed by transforming the distributions into a new set of generalisable functions, while characterising their energy spectrum under a particular energy landscape. We outline the combination of theoretical and machine learning based methods, for moving towards interpretable and generalisable models. Developing a theoretical framework to map from genotype to phenotype, we provide both supervised and unsupervised tools to operate over single cell RNA-Seq. data. We have generated a pipeline to identify relevant genes and cell types through Variational Autoencoders (VAEs), validating reconstructed gene expressions to prove the generative performance of the embeddings. The new interpretation of the information learned and extracted by the models de fines a label independent evaluation, particularly useful for unsupervised learning. Lastly, we introduce a novel transformation of the generative embeddings based on quantum and perturbation theory. Our contributions can and have been extended to new datasets, according to the nature of the tasks being explored. For instance, the combination of unsupervised learning and information theory can be applied to a variety of biological or medical data. We have trained several VAE models with additional cancer and metabolic data, proving to extract meaningful representations of the data. The perturbation theory transformation of the embedding can also lead to future research on the generative potential of Variational Autoencoders through a physics perspective, combining statistical and quantum mechanics. We believe that machine learning will only continue its fast expansion and growth through the development of more generalisable more interpretable models. "Prediction is very difficult, especially if it's about the future" Niels Boh

A Review and Analysis of Automatic Optical Inspection and Quality Monitoring Methods in Electronics Industry

Electronics industry is one of the fastest evolving, innovative, and most competitive industries. In order to meet the high consumption demands on electronics components, quality standards of the products must be well-maintained. Automatic optical inspection (AOI) is one of the non-destructive techniques used in quality inspection of various products. This technique is considered robust and can replace human inspectors who are subjected to dull and fatigue in performing inspection tasks. A fully automated optical inspection system consists of hardware and software setups. Hardware setup include image sensor and illumination settings and is responsible to acquire the digital image, while the software part implements an inspection algorithm to extract the features of the acquired images and classify them into defected and non-defected based on the user requirements. A sorting mechanism can be used to separate the defective products from the good ones. This article provides a comprehensive review of the various AOI systems used in electronics, micro-electronics, and opto-electronics industries. In this review the defects of the commonly inspected electronic components, such as semiconductor wafers, flat panel displays, printed circuit boards and light emitting diodes, are first explained. Hardware setups used in acquiring images are then discussed in terms of the camera and lighting source selection and configuration. The inspection algorithms used for detecting the defects in the electronic components are discussed in terms of the preprocessing, feature extraction and classification tools used for this purpose. Recent articles that used deep learning algorithms are also reviewed. The article concludes by highlighting the current trends and possible future research directions.Framework of the IQONIC Project; European Union’s Horizon 2020 Research and Innovation Program

Personality Identification from Social Media Using Deep Learning: A Review

Social media helps in sharing of ideas and information among people scattered around the world and thus helps in creating communities, groups, and virtual networks. Identification of personality is significant in many types of applications such as in detecting the mental state or character of a person, predicting job satisfaction, professional and personal relationship success, in recommendation systems. Personality is also an important factor to determine individual variation in thoughts, feelings, and conduct systems. According to the survey of Global social media research in 2018, approximately 3.196 billion social media users are in worldwide. The numbers are estimated to grow rapidly further with the use of mobile smart devices and advancement in technology. Support vector machine (SVM), Naive Bayes (NB), Multilayer perceptron neural network, and convolutional neural network (CNN) are some of the machine learning techniques used for personality identification in the literature review. This paper presents various studies conducted in identifying the personality of social media users with the help of machine learning approaches and the recent studies that targeted to predict the personality of online social media (OSM) users are reviewed

Друга міжнародна конференція зі сталого майбутнього: екологічні, технологічні, соціальні та економічні питання (ICSF 2021). Кривий Ріг, Україна, 19-21 травня 2021 року

Second International Conference on Sustainable Futures: Environmental, Technological, Social and Economic Matters (ICSF 2021). Kryvyi Rih, Ukraine, May 19-21, 2021.Друга міжнародна конференція зі сталого майбутнього: екологічні, технологічні, соціальні та економічні питання (ICSF 2021). Кривий Ріг, Україна, 19-21 травня 2021 року