Semantic segmentation of medical images with deep learning

Dissertação de mestrado em Engenharia InformáticaThe use of deep learning techniques in medical image analysis has been a subject of growing interest in recent years. One of the most important applications of these techniques is the detection and segmentation of tumors in histological images. This dissertation focused on investigating the use of deep learning models to segment tumors, with the aim of providing medical specialists with a tool that can help them make more precise diagnoses. Tumor growth patterns are an important histological characteristic that can provide information about the aggressiveness and degree of malignancy of a tumor. Specifically, the epithelial-mesenchymal transition on the tumor front is a pattern that has been shown to confer high aggressiveness and a great capacity to invade tissues and cause metastases, leading to a poor prognosis regarding the evolution of the tumor. Therefore, detecting and segmenting tumors in histological images can be a critical step in the diagnosis and treatment of tumors. The research process involved several steps, including preprocessing the images to prepare them for deep learning models. This step involved developing methods to enhance the quality of the images and make them suitable for training deep learning models. Two types of deep learning architectures, the U-Net and Tiramisu, were trained in a supervised way, and different types of loss functions were experimented with to measure their efficiency in controlling the training process. Additionally, different types of hyperparameters were tried, and the best value was chosen for each hyperparameter. Finally, the effectiveness of the models was evaluated and compared both qualitatively and quantitatively based on their performance in image segmentation. The results obtained show that deep learning models surpassed the initially predicted values and reached a value above 94% based on the training data. for the Interception over the Union metric. This result demonstrates the potential of deep learning techniques to detect and segment tumors in histological images and reinforces the importance of continuing to investigate this topic. The best results of the present work were achieved with total loss, as explained on page 89.A aplicação de técnicas de aprendizagem profunda na análise de imagens médicas tem sido alvo de um interesse crescente nos últimos anos. Uma das aplicações mais importantes destas técnicas é a deteção e segmentação de tumores em imagens histológicas. A presente dissertação fucou-se na investigação sobre a utilização de modelos de aprendizagem profunda para segmentar tumores, com o objetivo de fornecer aos especialistas médicos uma ferramenta que ajude a efetuar diagnósticos mais corretos. Os padrões de crescimento tumoral são uma característica histológica importante, que pode fornecer informação sobre a agressividade e grau de malignidade dum tumor. Especificamente, a transição epitelial-mesenquimal na frente do tumor é um padrão que confere alta agressividade e grande capacidade de invadir tecidos e causar metástases, conduzindo a um mau prognóstico sobre a evolução do tumor. Portanto, a detecção e segmentação de tumores em imagens histológicas é um passo crítico no diagnóstico e tratamento dos tumores. O trabalho desenvolvido decorreu em várias etapas, incluindo o pré-processamento das imagens para prepará-las para treinar os modelos de aprendizagem profunda. Essa etapa envolveu o desenvolvimento de métodos para melhorar a qualidade das imagens e torná-las adequadas para o treino de modelos de aprendizagem profunda. Dois tipos de modelo de aprendizagem profunda, U-Net e Tiramisu, foram treinados de forma supervisionada, e experimentaram-se diferentes tipos de função de perda para medir a sua eficácia no controlo do processo de treino. Adicionalmente, testaram-se diferentes tipos de hiperparâmetros e escolheu-se o melhor valor para cada hiperparâmetro a utilizar em futuras experiências. Finalmente, a eficácia dos modelos foi avaliada e comparada tanto qualitativamente como quantitativamente com base no seu desempenho na segmentação de imagens. Os resultados obtidos mostram que os modelos de aprendizagem profunda ultrapassaram os valores inicialmente previstos e alcançaram um valor acima de 94% com base nos dados de treinamento para a métrica de Intercepção sobre União. Este resultado demonstra o potencial das técnicas de aprendizagem profunda para detetar e segmentar tumores em imagens histológicas e reforça a importância de continuar a investigar este tópico. Os melhores resultados deste trabalho foram obtidos com a função de perda total, como se mostra na página 89

Complications of Orthognathic Surgery

Orthognathic surgery is a common approach for treatment of maxillofacial deformities. Sagittal split ramus osteotomy (SSRO) is one of the most common techniques used to treat various mandibular deformities. A LeFort I osteotomy is suggested in deformities of the maxilla and can be used along with SSRO or intra‐oral vertical ramus osteotomy (IVRO).The aim of orthognathic surgery is to improve function and facial appearance; this benefits the patient psychologically and socially. Common complications which may occur in orthognathic surgery include vascular disease, temporomandibular joints (TMJ) problems, nerve damage, infection, bone necrosis, periodontal disease, vision impairment, hearing problems, hair loss, and neuropsychiatric problems. Rarely complications could be fatal. Because of the wide range of complications the surgeon should keep prevention protocols in mind and be prepared to treat them should they occur. In this chapter, common complications of various osteotomies in the mandible and maxilla are discussed

The Effect of IELTS Listening Strategy Use on the Reduction of IELTS Listening Test Anxiety and on IELTS Listening Performance

The study investigates the impact of IELTS listening strategy use on the reduction of listening test anxiety and on the listening performance of the IELTS test takers in light of the data of 80 participants on the pretest and post-test IELTS listening along with the participants' score on pre-anxiety and post anxiety scale. So, drawing on the instruments including a proficiency test, pre/post-test, anxiety questionnaire, materials for strategy instruction, the participants were randomly divided into two groups: Control Group and Experimental Group, each including 40 participants. As per the procedure, after tackling their pre-listening performance and pre-anxiety score, one group was treated with IELTS-Listening related strategies and the other group was not treated, but both were administered listening test. The results of the study indicated that those treated with IELTS strategy outperformed ( t (78) = 4.57, p = .000, r = .460 ) those receiving no listening-related strategy. Furthermore, the results of a t-test run on the post-test of the groups anxiety arrived at a statistically significant difference (t (78) = 5.77, p = .000, r = .547), representing that the control group outperformed the experimental group. Also, Pearson Correlation done for finding out a potential relationship between anxiety and listening performance indicated a negative and weak to moderate relationship ((r (78) = -.26, p = .020). The pedagogical implications of the study are in detailed argued

Multiview Semi-Supervised Ranking for Automatic Image Annotation

International audienceMost photo sharing sites give their users the opportunity to manually label images. The labels collected that way are usually very incomplete due to the size of the image collections: most images are not labeled according to all the categories they belong to, and, conversely, many class have relatively few representative examples. Automated image systems that can deal with small amounts of labeled examples and unbalanced classes are thus necessary to better organize and annotate images. In this work, we propose a multiview semi-supervised bipartite ranking model which allows to leverage the information contained in unlabeled sets of images in order to improve the prediction performance, using multiple descriptions, or views of images. For each topic class, our approach first learns as many view-specific rankers as available views using the labeled data only. These rankers are then improved iteratively by adding pseudo-labeled pairs of examples on which all view-specific rankers agree over the ranking of examples within these pairs. We report on experiments carried out on the NUS-WIDE dataset, which show that the multiview ranking process improves predictive performances when a small number of labeled examples is available specially for unbalanced classes. We show also that our approach achieves significant improvements over a state-of the art semi-supervised multiview classification model

Assessment of Pocket Depth Changes in Treatment with Arch Bars: A Prospective Clinical Study

Introduction: It is suggested that arch bars act as plaque-retentive ligatures and therefore exert effects on periodontal tissues health. The aim of the present study was to assess pocket probing depth prior to placing arch bars and following their removal.Materials and Methods: Pocket probing depths were studied in the subjects who had arch bars for one month due to condylar fracture. Pocket depths were measured before placing arch bars, one month and 12 months after removing them. The mean of pocket depth was measured for each tooth. Periodontal probing depth was measured in six sites of each tooth .The mean pocket depth was calculated by the division of the sum of the pocket depths by the number of teeth for anterior and the posterior teeth in all subjects. Results: Eleven males and nine females were included in this study. No significant pocket depth differences was detected among the anterior and posterior of the mandible and maxilla before and after placing the arch bars. Results demonstrated a significant pocket depth increase in the anterior and posterior of both jaws one month following removal of the arch bars .The pocket depths were decreased following 12 months which were indicative of relative improvement at the sites. Conclusion: Arch bars can affect periodontium and pocket depths increased one month after releasing the arch bars. However, a significant improvement was detected following 12 months that suggested a partial reversible change in the pocket depths

Discourse Analysis in the ESL Classroom

This article attempts a user-friendly definition of discourse analysis. By defining it in this manner, the authors hope to encourage teachers to use it in their ESL classrooms. To this end, they suggest certain concrete measures that bring discourse analysis into the ESL classroom

Blood Products Use in Bimaxillary Orthognathic Surgeries: A Retrospective Study

Background: The purpose of this study was to determine the consumption of blood products during orthognathic surgeries by age, sex, blood group, operation time, and the amount of blood loss. Methods: This is a retrospective cohort study. Patients who underwent bimaxillary osteotomy were studied. The study focused on types and amount of blood loss, blood products used, and change in patient’s hemoglobin (Hb) and hematocrit (HCT). Patients’ demographic data, blood type, and duration of surgery were variables of the research. Results: A total of 133 patients (52 males and 81 females) with a mean age of 22.950 ± 4.241 years formed the study population. Average blood loss was 556.32 ± 245.05 ml and the average operating time was 259.96 ± 51.56 minutes. Results demonstrated that duration of the surgical and blood loss in males was higher than females. The mean of Hb and HCT levels before surgeries was 13.56 ± 1.30 and 40.47 ± 4.30, respectively, which significantly (P < 0.001) decreased to 11.969 ± 1.200 and 35.782 ± 3.800 1 day after the operations. The transfused blood products consisted of packed cells (5.4%), fresh frozen plasma (37.3%), and hydroxyethyl starch (57.3%). The percentage of patients who did not receive any transfusion was generally higher in the positive blood types than negative ones, with the highest percentage being in the AB+ group. Conclusions: A risk of using blood products particularly packed cells may increase if blood loss was above of 800 ml and surgical duration more than 300 minutes. The duration of orthognathic surgery may have a significant effect on blood loss and blood transfusions. It seems subjects with positive blood types may have a lower risk for transfusion

On the comparison of KM criteria classifications

AbstractThis study investigates the criteria for measuring knowledge management success among Malaysian organizations. Till date, no comprehensive sets of criteria were introduced by researchers. This survey, then, attempts to bridge the gap. The study population consists of 79 Malaysian organizations. This survey was conducted based on the reviewing of various literatures in the area of knowledge management. A review of the literature reveals that the criteria of measuring KM outcomes can be classified into five different classes: (1) Systematic knowledge programs; (2) Employee development; (3) Customer satisfaction; (4) Good external relationship; (5) Organizational success. In the current study, this sorting is used to shape a foundation in order to compare KM criteria classifications. Hence, this survey aims to uncover the most favoured classification within Malaysian organizations. The results of survey are then used to compare scores for five groups of criteria. According to the findings achieved from statistical analysis, Systematic knowledge programs stands first on the list of top KM criteria classifications and number two is Employee development. In addition, next ranks belong to Customer Satisfaction, Good external relationship, and Organizational success respectively. It is hoped that the results and findings of this survey will persuade businesses to perform KM initiatives properly in order to maximize the outcomes from KM programs

Multiple Intelligence and EFL Learners' Reading Comprehension

The second half of the twentieth century can be called the age of individualization when individual values and differences are recognized and respected. Intelligence is among the various aspects of individual differences which affect education and language learning. As such, the present study aimed at investigating the relationship between Multiple Intelligence and Reading Comprehension Abilities of Iranian EFL learners. For the purpose of study, 117 senior English students were randomly selected. After administering two types of instruments including MIDAS Adults (Shearer, 1996) and Reading Comprehension Section of TOEFL (2005, Longman), the data were collected and analyzed. The results indicated that all types of the learners’ MI profile have significant relationship with the reading comprehension scores and the Verbal-Linguistic Intelligence is the most significant predictor of the learners’ reading comprehension abilities, while Visual-Spatial and Interpersonal Intelligences are the second and third predictors of the learners’ reading comprehension respectively. Furthermore, Intrapersonal and Kinesthetic Intelligences could not predict the reading comprehension of the learners.Keywords: Multiple Intelligence, Verbal Intelligence, Visual Intelligence, Interpersonal Intelligence, Musical Intelligence, Kinesthetic Intelligence, Reading Comprehensio