Synthesization and reconstruction of 3D faces by deep neural networks

The past few decades have witnessed substantial progress towards 3D facial modelling and reconstruction as it is high importance for many computer vision and graphics applications including Augmented/Virtual Reality (AR/VR), computer games, movie post-production, image/video editing, medical applications, etc. In the traditional approaches, facial texture and shape are represented as triangle mesh that can cover identity and expression variation with non-rigid deformation. A dataset of 3D face scans is then densely registered into a common topology in order to construct a linear statistical model. Such models are called 3D Morphable Models (3DMMs) and can be used for 3D face synthesization or reconstruction by a single or few 2D face images. The works presented in this thesis focus on the modernization of these traditional techniques in the light of recent advances of deep learning and thanks to the availability of large-scale datasets. Ever since the introduction of 3DMMs by over two decades, there has been a lot of progress on it and they are still considered as one of the best methodologies to model 3D faces. Nevertheless, there are still several aspects of it that need to be upgraded to the "deep era". Firstly, the conventional 3DMMs are built by linear statistical approaches such as Principal Component Analysis (PCA) which omits high-frequency information by its nature. While this does not curtail shape, which is often smooth in the original data, texture models are heavily afflicted by losing high-frequency details and photorealism. Secondly, the existing 3DMM fitting approaches rely on very primitive (i.e. RGB values, sparse landmarks) or hand-crafted features (i.e. HOG, SIFT) as supervision that are sensitive to "in-the-wild" images (i.e. lighting, pose, occlusion), or somewhat missing identity/expression resemblance with the target image. Finally, shape, texture, and expression modalities are separately modelled by ignoring the correlation among them, placing a fundamental limit to the synthesization of semantically meaningful 3D faces. Moreover, photorealistic 3D face synthesis has not been studied thoroughly in the literature. This thesis attempts to address the above-mentioned issues by harnessing the power of deep neural network and generative adversarial networks as explained below: Due to the linear texture models, many of the state-of-the-art methods are still not capable of reconstructing facial textures with high-frequency details. For this, we take a radically different approach and build a high-quality texture model by Generative Adversarial Networks (GANs) that preserves details. That is, we utilize GANs to train a very powerful generator of facial texture in the UV space. And then show that it is possible to employ this generator network as a statistical texture prior to 3DMM fitting. The resulting texture reconstructions are plausible and photorealistic as GANs are faithful to the real-data distribution in both low- and high- frequency domains. Then, we revisit the conventional 3DMM fitting approaches making use of non-linear optimization to find the optimal latent parameters that best reconstruct the test image but under a new perspective. We propose to optimize the parameters with the supervision of pretrained deep identity features through our end-to-end differentiable framework. In order to be robust towards initialization and expedite the fitting process, we also propose a novel self-supervised regression-based approach. We demonstrate excellent 3D face reconstructions that are photorealistic and identity preserving and achieve for the first time, to the best of our knowledge, facial texture reconstruction with high-frequency details. In order to extend the non-linear texture model for photo-realistic 3D face synthesis, we present a methodology that generates high-quality texture, shape, and normals jointly. To do so, we propose a novel GAN that can generate data from different modalities while exploiting their correlations. Furthermore, we demonstrate how we can condition the generation on the expression and create faces with various facial expressions. Additionally, we study another approach for photo-realistic face synthesis by 3D guidance. This study proposes to generate 3D faces by linear 3DMM and then augment their 2D rendering by an image-to-image translation network to the photorealistic face domain. Both works demonstrate excellent photorealistic face synthesis and show that the generated faces are improving face recognition benchmarks as synthetic training data. Finally, we study expression reconstruction for personalized 3D face models where we improve generalization and robustness of expression encoding. First, we propose a 3D augmentation approach on 2D head-mounted camera images to increase robustness to perspective changes. And, we also propose to train generic expression encoder network by populating the number of identities with a novel multi-id personalized model training architecture in a self-supervised manner. Both approaches show promising results in both qualitative and quantitative experiments.Open Acces

Evaluation of Joint Multi-Instance Multi-Label Learning For Breast Cancer Diagnosis

Multi-instance multi-label (MIML) learning is a challenging problem in many aspects. Such learning approaches might be useful for many medical diagnosis applications including breast cancer detection and classification. In this study subset of digiPATH dataset (whole slide digital breast cancer histopathology images) are used for training and evaluation of six state-of-the-art MIML methods. At the end, performance comparison of these approaches are given by means of effective evaluation metrics. It is shown that MIML-kNN achieve the best performance that is %65.3 average precision, where most of other methods attain acceptable results as well

Detection of High-Risk Human Papillomavirus by Chromogenic in Situ Hybridization Method in Liquid-Based Cervicovaginal Cytology Specimens with Atypical Squamous Cells of Undetermined Significance

Background: Cervical cancer is the second most common cancer after breast cancer in women worldwide. Owing to comprehensive cervical screening programs, a significant decrease has been observed in the incidence of invasive cervical cancers. Human Papillomavirus (HPV) is the underlying etiology in 99.7% of the cases of cervical cancer and is a major risk factor for the development of precancerous and cancerous cervical lesions. Chromogenic in situ hybridization method (CISH) is one of the methods used to detect high risk HPV in liquid -based smears. Aims: In this study, we conducted an archive search in the department of pathology and we used chromogenic in situ hybridization technique to investigate whether HPV DNA was present in liquid -based smears  of 68 patients who had been  found to have atypical squamous cells of undetermined significance (ASCUS) and whether a cervical intraepithelial lesion was present in the biopsy samples of patients who were positive for HPV DNA.Methods and Material:. We prepared additional samples from liquid -based preparations obtained by the ThinPrep method. The CISH procedure was performed on the Benchmark Automated Slide Stainer (Ventana) according to the manufacturer’s standard protocol.Results and Conclusions: We found HPV DNA in  the samples of 10 patients by using INFORM HPV III high risk (16, 18, 31, 33, 35, 39, 45, 51, 52, 56, 58, 68, and 70) (Ventana, Tucson, AZ) probe, with CISH method on each additional sample, and biopsy  results of the respective  patients, indicated  low grade squamous intraepithelial lesion (LGSIL) in 8 patients and high grade squamous intraepithelial lesion (HGSIL) in 2 cases. Keywords: Human papillomavirus, chromogenic in situ hybridization, atypical squamous cells of undetermined significance DOI: 10.7176/JHMN/80-10 Publication date:September 30th 202

ADAPTATION OF INTERNET ETHICAL ATTITUDE SCALE TO UNIVERSITY STUDENTS: COMPARISON OF INTERNET ETHICAL ATTITUDES ACCORDING TO STUDENTS’ DEPARTMENTS

Internet ethics is clearly an issue that concerns every Internet user closely no matter what their purpose is. Teachers are undoubtedly the ones to assume the most critical task at this stage. As persons who will shape future generations, teachers are expected to fully grasp the importance of Internet ethics in school years and display model behavior using the Internet fairly. The goal of this study is to adapt the Internet ethical attitude scale for students from secondary school to university and compare the ethical attitudes of participant students according to their genders and departments. The study was carried out with 294 students. The data was collected through the Internet Ethical Attitude Scale (IEAS) and personal information form designed by researchers. The results showed that female students had higher total scores and sub-factor scores in IEAS than male students. It is possible to say that female students are more conscientious than male students in the issue of Internet ethical attitudes. As in other factors, attitudes related to “homework plagiarism” are higher in female students than male students

GANFIT: Generative adversarial network fitting for high fidelity 3D face reconstruction

In the past few years, a lot of work has been done to- wards reconstructing the 3D facial structure from single images by capitalizing on the power of Deep Convolutional Neural Networks (DCNNs). In the most recent works, differentiable renderers were employed in order to learn the relationship between the facial identity features and the parameters of a 3D morphable model for shape and texture. The texture features either correspond to components of a linear texture space or are learned by auto-encoders directly from in-the-wild images. In all cases, the quality of the facial texture reconstruction of the state-of-the-art methods is still not capable of modeling textures in high fidelity. In this paper, we take a radically different approach and harness the power of Generative Adversarial Networks (GANs) and DCNNs in order to reconstruct the facial texture and shape from single images. That is, we utilize GANs to train a very powerful generator of facial texture in UV space. Then, we revisit the original 3D Morphable Models (3DMMs) fitting approaches making use of non-linear optimization to find the optimal latent parameters that best reconstruct the test image but under a new perspective. We optimize the parameters with the supervision of pretrained deep identity features through our end-to-end differentiable framework. We demonstrate excellent results in photorealistic and identity preserving 3D face reconstructions and achieve for the first time, to the best of our knowledge, facial texture reconstruction with high-frequency details

Comparison of HER-2 Amplification with Clinicopathological and Prognostic Parameters in Metastatic Gastric Carcinomas

Introduction: HER2 overexpression is present in 7 to 34% of gastric carcinomas. Several studies have demonstrated associations between HER2 overexpression and clinicopathological variables, including tumor depth, lymph node metastasis, and intestinal morphology. HER2 overexpression has been linked to shorter survival. Aim: In this study, we aimed to determine the frequency of HER2 overexpression in patients with metastatic gastric carcinoma referred to our clinic, to assess HER2 expression using immunohistochemistry (IHC) or silver in situ hybridization (SISH) and to demonstrate potential associations between HER2 expression and histopathological parameters. Materials and Methods: In this study, we assessed archival samples from 120 patients diagnosed with metastatic gastric carcinoma between 2015 and 2019 and tested for HER2 status. Samples had been obtained by endoscopic biopsy in 84 patients and gastric resection in 36 patients, whereas 36 patients were diagnosed in other health facilities and were referred to our department for consulting. Hematoxylin-eosin stained preparations were reassessed, and diagnoses were confirmed based on World Health Organization (WHO), and Lauren classifications and HER2 results were compared to previous results. HER2 status was assessed by immunohistochemistry (IHC) or silver in situ hybridization (SISH) in available paraffin-embedded tumor sections. Associations between HER2 expression levels and age, sex, tumor location,  size and histological type of the tumor, lymphovascular,  perineural and perinodal invasion and perinodal invasion, lymph node metastasis, and site of metastasis. Results: 84 (70%) out of 120 patients with metastatic gastric carcinoma were male, and 36  (30%) were female, and the mean age was  60.7 years  (age range: 21-90 years).  84 patients had undergone an endoscopic biopsy, and 36 patients had undergone gastric resection, whereas 39 patients were diagnosed in other health facilities and were referred to our department for consulting. In total 31(25.8%) out of 120 subjects tested positive for HER2 overexpression Comparisons of histological patterns, according to Lauren's classification, indicated that intestinal type was predominant in both groups, and none of the diffuse gastric carcinomas was HER2 positive. A statistically significant intergroup difference was found with respect to the prevalence of diffuse gastric carcinoma (p=0.03). Conclusion: In our study, HER-2 gene amplification (25.8%) is compatible with the literature. Her2 positive tumors were mostly located proximally and were not observed in diffuse type. In this respect, they were found statistically significant. Keywords:Her2,SISH,Gastric  Carcinoma Original Article: DOI: 10.7176/JHMN/79-07 Publication date:August 31st 202

Essays on digital marketing strategies: an analytical investigation

In the first paper, we have focused on the publisher strategies against ad-blockers. Ad-blocking technologies are increasingly adopted by consumers. For content producers whose major source of revenue is advertising, this creates a challenge for survival. Facing the threat from ad-blockers, content managers are implementing a range of strategies, ranging from declining to serve customers who install ad-blockers to asking them to be paid subscribers. In this study, we consider the impact of content managers’ strategies against the ad-blockers based on the differentiation of content in online media. Our findings suggest that publishers decide on their strategies against ad-blockers based on their content production cost and content differentiation. A publisher that produces differentiated content for a low cost, such as personal blogs, follows the “content wall” strategy where they do not give access to those who use ad-blockers while other publishers follow the “pay to avoid” strategy where they offer an ad-free version of the website. Content differentiation is also important for the publishers that produce content for a high cost. In this case, content production is profitable only when the content is differentiated. We also show that competition does not increase content quality all the time. A monopolistic company that produces its content for a low cost decreases its content quality when a new publisher produces substitutable content. Last but not least, when the ad-giving firm is a decision maker, the decision process of the publisher is restricted. In the second paper, we focus on discounting strategies through different sales channels. Customer engagement with mobile devices has changed customers’ habits in online purchasing, in particular the usage of mobile apps for shopping increases the customer lifetime value (CLV). Based on a quasi-field experiment, we show that once customers start using their mobile devices to purchase, they adopt this channel and keep purchasing through it. Also, we support the literature showing that purchasing over mobile apps increases the customer lifetime value (CLV). Hence, we suggest the idea that online retailers should steer their customers to the mobile channel by offering a permanent discount over the mobile app. Although this strategy decreases the short-term income, it may increase the CLV. Based on this suggestion, we develop a probability-based CLV model to show to what type of customers an online retailer should offer a discount over the mobile app as well as the optimal value of that discount. Following an analytical modeling approach, we are able to show that the online retailer should offer such a discount to a customer who is either very likely or unlikely to increase her purchasing probability. Also, the firm could offer such a discount to encourage the customer to switch to the mobile app. Online retailers should not offer the discount to those who already have a high purchasing probability because their CLV is already huge. Last but not least, an online retailer should not follow this strategy to gain new customers (customer acquisition) but rather apply it to increase customer retention

Die Anwendung der Cleavage-Theorie auf die Türkei

In einem historischen Umriss entlang mehrere relevanten Ereignisse wird detailliert auf die Ursachen und Entwicklung der Konfliktlinien nach der Cleavage-Theorie von Lipset und Rok-kan (1967) im Osmanischen Reich und in der Türkei eingegangen. Von den vier Konfliktlinien gemäß der Cleavage-Theorie ist für die Türkei einzig die zentrum-periphere Konfliktlinie von Bedeutung. Die Zentrum-Peripherie-Konfliktlinie versteht sich primär als ein kultureller Mo-dernisierungskonflikt. Diese findet ihren Ausdruck in einer türkisch-kurdischen Konfliktlinie und in einer säkular-islamistischen Konfliktlinie