Lung cancer is the most commonly diagnosed cancer in the world and its finding is mainly incidental. New technologies and more specifically artificial intelligence has lately acquired big interest in the medical field as it can automate or bring new information to the medical staff. Many research have been done on the detection or classification of lung cancer. These works are done on local region of interest but only a few of them have been done looking at a full CT-scan. The aim of this thesis was to assess lung damages from CT images using new machine learning methods. First, single predictors had been learned by a 3D resnet architecture: cancer, emphysema, and opacities. Emphysema was learned by the network reaching an AUC of 0.79 whereas cancer and opacity predictions were not really better than chance AUC = 0.61 and AUC = 0.61. Secondly, a multi-task network was used to predict the factors altogether. A training with no prior knowledge and a transfer learning approach using self-supervision were compared. The transfer learning approach showed similar results in the multi-task approach for emphysema with AUC=0.78 vs 0.60 without pre-training and opacities with an AUC=0.61. Moreover using the pre-training approach enabled the network to reach the same performance as each of single factor predictor but with only one multi-task network which saves a lot of computational time. Finally a risk score can be derived from the training to use this information in a clinical context.

In recent years, the field of computer vision has seen remarkable advancements, particularly with the rise of deep learning techniques. Object detection, a challenging task in image analysis, has benefited from these developments. This thesis investigates the application of object detection models, specifically You Only Look Once (YOLO), in the context of food recognition and health assessment based on the Swedish plate model. The study aims to assess the effectiveness of YOLO models in predicting the healthiness of food compositions according to the guidelines provided by the Swedish plate model. The research utilizes a custom dataset comprising 3707 images with 42 different food classes. Various preprocessing- and augmentation techniques are applied to enhance dataset quality and model robustness. The performance of the three YOLO models (YOLOv7, YOLOv8, and YOLOv9) are evaluated using precision, recall, mean Average Precision (mAP), and F1 score metrics. Results indicate that YOLOv8 showed higher performance, making it the recommended choice for further implementation in dietary assessment and health promotion initiatives. The study contributes to the understanding of how deep learning models can be leveraged for food recognition and health assessment. Overall, this thesis underscores the potential of deep learning in advancing computational approaches to dietary assessment and promoting healthier eating habits.

For several more complex tasks, such as semantic role labeling (SRL), large annotated datasets are necessary. For smaller and lower-resource languages, these are not readily available. As a way to overcome this data bottleneck, this thesis investigates the possibilities of using transfer learning from a high-resource language to a low-resource language, and then perform zero-shot SRL on the low-resource language. We additionally investigate if the transfer-learning can be improved by freezing the parameters of a layer in the pre-trained model, leveraging the model to instead focus on learning the parameters of the layers necessary for the task. By training models in English and then evaluating on Spanish, Catalan, German and Chinese CoNLL-2009 data, we find that transfer learning zero-shot SRL can be an effective technique, and in certain cases outperform models trained on low amounts of data. We also find that the results improve when freezing parameters of the lower layers of the model, the layers focused on surface tasks, as this allowed the model to improve the layers necessary for SRL.

Multi-temporal image analysis has been widely used in many applications such as urban monitoring, disaster management, and agriculture. With the development of the remote sensing technology, the new generation remote sensing satellite images with High/ Very High spatial resolution (HR/VHR) are now available. Compared to the traditional low/medium spatial resolution images, the detailed information of ground objects can be clearly analyzed in the HR/VHR images. Classical methods of multi-temporal image analysis deal with the images at pixel level and have worked well on low/medium resolution images. However, they provide sub-optimal results on new generation images due to their limited capability of modeling complex spatial and spectral information in the new generation products. Although significant number of object-based methods have been proposed in the last decade, they depend on suitable segmentation scale for diverse kinds of objects present in each temporal image. Thus their capability to express contextual information is limited. Typical spatial properties of last generation images emphasize the need of having more flexible models for object representation. Another drawback of the traditional methods is the difficulty in transferring knowledge learned from one specific problem to another. In the last few years, an interesting development is observed in the machine learning/computer vision field. Deep learning, especially Convolution Neural Networks (CNNs) have shown excellent capability to capture object level information and in transfer learning. By 2015, deep learning achieved state-of-the-art performance in most computer vision tasks. Inspite of its success in computer vision fields, the application of deep learning in multi-temporal image analysis saw slow progress due to the requirement of large labeled datasets to train deep learning models. However, by the start of this PhD activity, few works in the computer vision literature showed that deep learning possesses capability of transfer learning and training without labeled data. Thus, inspired by the success of deep learning, this thesis focuses on developing deep learning based methods for unsupervised/semi-supervised multi-temporal image analysis. This thesis is aimed towards developing methods that combine the benefits of deep learning with the traditional methods of multi-temporal image analysis. Towards this direction, the thesis first explores the research challenges that incorporates deep learning into the popular unsupervised change detection (CD) method - Change Vector Analysis (CVA) and further investigates the possibility of using deep learning for multi-temporal information extraction. The thesis specifically: i) extends the paradigm of unsupervised CVA to novel Deep CVA (DCVA) by using a pre-trained network as deep feature extractor; ii) extends DCVA by exploiting Generative Adversarial Network (GAN) to remove necessity of having a pre-trained deep network; iii) revisits the problem of semi-supervised CD by exploiting Graph Convolutional Network (GCN) for label propagation from the labeled pixels to the unlabeled ones; and iv) extends the problem statement of semantic segmentation to multi-temporal domain via unsupervised deep clustering. The effectiveness of the proposed novel approaches and related techniques is demonstrated on several experiments involving passive VHR (including Pleiades), passive HR (Sentinel-2), and active VHR (COSMO-SkyMed) datasets. A substantial improvement is observed over the state-of-the-art shallow methods.

DeepPaint is a semi-automated segmentation tool that utilises a U-net architecture to performbinary segmentation. To maximise the model’s performance and minimise user time, it isadvisable to apply Transfer Learning (TL) and reuse a model trained on a similar segmentationtask. However, due to the sensitivity of medical data and the unique properties of certainsegmentation tasks, TL is not feasible for some applications. In such circumstances, SelfSupervised Learning (SSL) emerges as the most viable option to minimise the time spent inDeepPaint by a user. Various pretext tasks, exploring both corruption segmentation and corruption restoration, usingsuperpixels and square patches, were designed and evaluated. With a limited number ofiterations in both the pretext and downstream tasks, significant improvements across fourdifferent datasets were observed. The results reveal that SSL models, particularly those pretrained on corruption segmentation tasks where square patches were corrupted, consistentlyoutperformed models without pre-training, with regards to a cumulative Dice SimilarityCoefficient (DSC). To examine whether a model could learn relevant features from a pretext task, Centred KernelAlignment (CKA) was used to measure the similarity of feature spaces across a model's layersbefore and after fine-tuning on the downstream task. Surprisingly, no significant positivecorrelation between downstream DSC and CKA was observed in the encoder, likely due to thelimited fine-tuning allowed. Furthermore, it was examined whether pre-training on the entiredataset, as opposed to only the training subset, yielded different downstream results. Asexpected, significantly higher DSC in the downstream task is more likely if the model hadaccess to all data during the pretext task. The differences in downstream segmentationperformance between models that accessed different data subsets during pre-training variedacross datasets.

O aprendizado por reforço é uma técnica bem sucedida, porém lenta, para treinar agentes autônomos. Algumas soluções baseadas em políticas parciais podem ser usadas para acelerar o aprendizado e para transferir comportamentos aprendidos entre tarefas encapsulando uma política parcial. No entanto, geralmente essas políticas parciais são específicas para uma única tarefa, não levam em consideração recursos semelhantes entre tarefas e podem não corresponder exatamente a um comportamento ideal quando transferidas para outra tarefa diferente. A transferência descuidada pode fornecer más soluções para o agente, dificultando o processo de aprendizagem. Sendo assim, este trabalho propõe uma maneira de descobrir e reutilizar de modo probabilístico políticas parciais orientadas a objetos aprendidas, a fim de permitir melhores escolhas de atuação para o agente em múltiplas tarefas diferentes. A avaliação experimental mostra que a proposta é capaz de aprender e reutilizar com sucesso políticas parciais em diferentes tarefas. / Reinforcement Learning is a successful yet slow technique to train autonomous agents. Option-based solutions can be used to accelerate learning and to transfer learned behaviors across tasks by encapsulating a partial policy. However, commonly these options are specific for a single task, do not take in account similar features between tasks and may not correspond exactly to an optimal behavior when transferred to another task. Therefore, careless transfer might provide bad options to the agent, hampering the learning process. This work proposes a way to discover and reuse learned objectoriented options in a probabilistic way in order to enable better actuation choices to the agent in multiple different tasks. The experimental evaluation show that the proposal is able to learn and successfully reuse options across different tasks.

A Mineração de Opiniões é um processo que tem por objetivo extrair as opiniões e suas polaridades de sentimentos expressas em textos em língua natural. Essa área de pesquisa tem ganhado destaque devido ao volume de opiniões que os usuários compartilham na Internet, como revisões em sites de e-commerce, rede sociais e tweets. A Mineração de Opiniões baseada em Aspectos é uma alternativa promissora para analisar a polaridade do sentimento em um maior nível de detalhes. Os métodos tradicionais para extração de aspectos e classificação de sentimentos exigem a participação de especialistas de domínio para criar léxicos ou definir regras de extração para diferentes idiomas e domínios. Além disso, tais métodos usualmente exploram algoritmos de aprendizado supervisionado, porém exigem um grande conjunto de dados rotulados para induzir um modelo de classificação. Os desafios desta tese de doutorado estão relacionados a como diminuir a necessidade de grande esforço humano tanto para rotular dados, quanto para tratar a dependência de domínio para as tarefas de extração de aspectos e classificação de sentimentos dos aspectos para Mineração de Opiniões. Para reduzir a necessidade de grande quantidade de exemplos rotulados foi proposta uma abordagem semissupervisionada, denominada por Aspect-based Sentiment Propagation on Heterogeneous Networks (ASPHN) em que são propostas representações de textos nas quais os atributos linguísticos, os aspectos candidatos e os rótulos de sentimentos são modelados por meio de redes heterogêneas. Para redução dos esforços para construir recursos específicos de domínio foi proposta uma abordagem baseada em aprendizado por transferência entre domínios denominada Cross-Domain Aspect Label Propagation through Heterogeneous Networks (CD-ALPHN) que utiliza dados rotulados de outros domínios para suportar tarefas de aprendizado em domínios sem dados rotulados. Nessa abordagem são propostos uma representação em uma rede heterogênea e um método de propagação de rótulos. Os vértices da rede são os aspectos rotulados do domínio de origem, os atributos linguísticos e os candidatos a aspectos do domínio alvo. Além disso, foram analisados métodos de extração de aspectos e propostas algumas variações para considerar cenários nãosupervisionados e independentes de domínio. As soluções propostas nesta tese de doutorado foram avaliadas e comparadas as do estado-da-arte utilizando coleções de revisões de diferentes produtos e serviços. Os resultados obtidos nas avaliações experimentais são competitivos e demonstram que as soluções propostas são promissoras. / Opinion Mining is a process that aims to extract opinions and their sentiment polarities expressed in natural language texts. This area of research has been in the highlight because of the volume of opinions that users share on the available visualization means on the Internet (reviews on e-commerce sites, social networks, tweets, others). Aspect-based Opinion Mining is a promising alternative for analyzing the sentiment polarity on a high level of detail. The traditional methods for aspect extraction and sentiment classification require the participation of domain experts to create lexicons or define extraction rules for different languages and domains. In addition, such methods usually exploit supervised machine learning algorithms, but require a large set of labeled data to induce a classification model. The challenges of this doctoral thesis are related on to how to reduce the need for great human effort both: (i) to label data; and (ii) to treat domain dependency for the tasks of aspect extraction and aspect sentiment classification for Opinion Mining. In order to reduce the need for a large number of labeled examples, a semi-supervised approach was proposed, called Aspect-based Sentiment Propagation on Heterogeneous Networks (ASPHN). In this approach, text representations are proposed in which linguistic attributes, candidate aspects and sentiment labels are modeled by heterogeneous networks. Also, a cross-domain learning approach called Cross-Domain Aspect Label Propagation through Heterogeneous Networks (CD-ALPHN) is proposed in order to reduce efforts to build domain-specific resources, This approach uses labeled data from other domains to support learning tasks in domains without labeled data. A representation in a heterogeneous network and a label propagation method are proposed in this cross-domain learning approach. The vertices of the network are the labeled aspects of the source domain, the linguistic attributes, and the candidate aspects of the target domain. In addition, aspect extraction methods were analyzed and some variations were proposed to consider unsupervised and domain independent scenarios. The solutions proposed in this doctoral thesis were evaluated and compared to the state-of-the-art solutions using collections of different product and service reviews. The results obtained in the experimental evaluations are competitive and demonstrate that the proposed solutions are promising.

Para desenvolver um novo medicamento, pesquisadores devem analisar os alvos biológicos de uma dada doença, descobrir e desenvolver candidatos a fármacos para este alvo biológico, realizando em paralelo, testes em laboratório para validar a eficiência e os efeitos colaterais da substância química. O estudo quantitativo da relação estrutura-atividade (QSAR) envolve a construção de modelos de regressão que relacionam um conjunto de descritores de um composto químico e a sua atividade biológica com relação a um ou mais alvos no organismo. Os conjuntos de dados manipulados pelos pesquisadores para análise QSAR são caracterizados geralmente por um número pequeno de instâncias e isso torna mais complexa a construção de modelos preditivos. Nesse contexto, a transferência de conhecimento utilizando informações de outros modelos QSAR\'s com mais dados disponíveis para o mesmo alvo biológico seria desejável, diminuindo o esforço e o custo do processo para gerar novos modelos de descritores de compostos químicos. Este trabalho apresenta uma abordagem de transferência de aprendizagem indutiva (por parâmetros), tal proposta baseia-se em uma variação do método de Regressão por Vetores Suporte adaptado para transferência de aprendizagem, a qual é alcançada ao aproximar os modelos gerados separadamente para cada tarefa em questão. Considera-se também um método de transferência de aprendizagem por instâncias, denominado de TrAdaBoost. Resultados experimentais mostram que as abordagens de transferência de aprendizagem apresentam bom desempenho quando aplicadas a conjuntos de dados de benchmark e a conjuntos de dados químicos / To develop a new medicament, researches must analyze the biological targets of a given disease, discover and develop drug candidates for this biological target, performing in parallel, biological tests in laboratory to validate the effectiveness and side effects of the chemical substance. The quantitative study of structure-activity relationship (QSAR) involves building regression models that relate a set of descriptors of a chemical compound and its biological activity with respect to one or more targets in the organism. Datasets manipulated by researchers to QSAR analysis are generally characterized by a small number of instances and this makes it more complex to build predictive models. In this context, the transfer of knowledge using information other\'s QSAR models with more data available to the same biological target would be desirable, nince its reduces the effort and cost to generate models of chemical descriptors. This work presents an inductive learning transfer approach (by parameters), such proposal is based on a variation of the Vector Regression method Adapted support for learning transfer, which is achieved by approaching the separately generated models for each task. It is also considered a method of learning transfer by instances, called TrAdaBoost. Experimental results show that learning transfer approaches perform well when applied to some datasets of benchmark and dataset chemical

Background. Nowadays, information retrieval system become more and more popular, it helps people retrieve information more efficiently and accelerates daily task. Within this context, Image processing technology play an important role that help transcribing content in printed or handwritten documents into digital data in information retrieval system. This transcribing procedure is called document digitization. In this transcribing procedure, image processing technique such as layout analysis and word recognition are employed to segment the document content and transcribe the image content into words. At this point, a Swedish company (ArkivDigital® AB) has a demand to transcribe their document data into digital data. Objectives. In this study, the aim is to find out effective solution to extract document layout regard to the Swedish handwritten historical documents, which are featured by their tabular forms containing the handwritten content. In this case, outcome of application of OCRopus, OCRfeeder, traditional image processing techniques, machine learning techniques on Swedish historical hand-written document is compared and studied. Methods. Implementation and experiment are used to develop three comparative solutions in this study. One is Hessian filtering with mask operation; another one is Gabor filtering with morphological open operation; the last one is Gabor filtering with machine learning classification. In the last solution, different alternatives were explored to build up document layout extraction pipeline. Hessian filter and Gabor filter are evaluated; Secondly, filter images with the better filter evaluated at previous stage, then refine the filtered image with Hough line transform method. Third, extract transfer learning feature and custom feature. Fourth, feed classifier with previous extracted features and analyze the result. After implementing all the solutions, sample set of the Swedish historical handwritten document is applied with these solutions and compare their performance with survey. Results. Both open source OCR system OCRopus and OCRfeeder fail to deliver the outcome due to these systems are designed to handle general document layout instead of table layout. Traditional image processing solutions work in more than a half of the cases, but it does not work well. Combining traditional image process technique and machine leaning technique give the best result, but with great time cost. Conclusions. Results shows that existing OCR system cannot carry layout analysis task in our Swedish historical handwritten document. Traditional image processing techniques are capable to extract the general table layout in these documents. By introducing machine learning technique, better and more accurate table layout can be extracted, but comes with a bigger time cost. / Scalable resource-efficient systems for big data analytics

<p>In recent years, deep learning models have become popular in the real-time embedded application, but there are many complexities for hardware deployment because of limited resources such as memory, computational power, and energy. Recent research in the field of deep learning focuses on reducing the model size of the Convolution Neural Network (CNN) by various compression techniques like Architectural compression, Pruning, Quantization, and Encoding (e.g., Huffman encoding). Network pruning is one of the promising technique to solve these problems.</p> <p>This thesis proposes methods to prune the convolution neural network (SqueezeNet) without introducing network sparsity in the pruned model. </p> <p>This thesis proposes three methods to prune the CNN to decrease the model size of CNN without a significant drop in the accuracy of the model.</p> <p>1: Pruning based on Taylor expansion of change in cost function Delta C.</p> <p>2: Pruning based on L₂ normalization of activation maps.</p> <p>3: Pruning based on a combination of method 1 and method 2.</p><p>The proposed methods use various ranking methods to rank the convolution kernels and prune the lower ranked filters afterwards SqueezeNet model is fine-tuned by backpropagation. Transfer learning technique is used to train the SqueezeNet on the CIFAR-10 dataset. Results show that the proposed approach reduces the SqueezeNet model by 72% without a significant drop in the accuracy of the model (optimal pruning efficiency result). Results also show that Pruning based on a combination of Taylor expansion of the cost function and L₂ normalization of activation maps achieves better pruning efficiency compared to other individual pruning criteria and most of the pruned kernels are from mid and high-level layers. The Pruned model is deployed on BlueBox 2.0 using RTMaps software and model performance was evaluated.</p><p></p>