PeMNet for Pectoral Muscle Segmentation

As an important imaging modality, mammography is considered to be the global gold standard for early detection of breast cancer. Computer-Aided (CAD) systems have played a crucial role in facilitating quicker diagnostic procedures, which otherwise could take weeks if only radiologists were involved. In some of these CAD systems, breast pectoral segmentation is required for breast region partition from breast pectoral muscle for specific analysis tasks. Therefore, accurate and efficient breast pectoral muscle segmentation frameworks are in high demand. Here, we proposed a novel deep learning framework, which we code-named PeMNet, for breast pectoral muscle segmentation in mammography images. In the proposed PeMNet, we integrated a novel attention module called the Global Channel Attention Module (GCAM), which can effectively improve the segmentation performance of Deeplabv3+ using minimal parameter overheads. In GCAM, channel attention maps (CAMs) are first extracted by concatenating feature maps after paralleled global average pooling and global maximum pooling operation. CAMs are then refined and scaled up by multi-layer perceptron (MLP) for elementwise multiplication with CAMs in next feature level. By iteratively repeating this procedure, the global CAMs (GCAMs) are then formed and multiplied elementwise with final feature maps to lead to final segmentation. By doing so, CAMs in early stages of a deep convolution network can be effectively passed on to later stages of the network and therefore leads to better information usage. The experiments on a merged dataset derived from two datasets, INbreast and OPTIMAM, showed that PeMNet greatly outperformed state-of-the-art methods by achieving an IoU of 97.46%, global pixel accuracy of 99.48%, Dice similarity coefficient of 96.30%, and Jaccard of 93.33%, respectively.

Current Advances in Computational biology of Lung Cancer

Lung cancer is the main basis of cancer death amongst men and women, making up almost 25% of the world’s total cancer deaths. Lung cancer described for nearly 1.6 million deaths in 2012 and 1.80 million deaths in 2020. Small cell lung cancer and non-small-cell lung cancer are the two key categories of Lung cancer. The signs of lung cancer include hemoptysis, weight loss, shortness of breath and chest pain. Lung cancer treated by chemotherapy, surgery and CT scan. In this review paper, one of the most crucial zones aiming lung cancer diagnosis has been discussed. Computer-aided diagnosis (CAD) systems adapted for lung cancer can increase the patients’ survival chances. A typical CAD system for lung cancer functions in the fields of lung segmentation, detecting lung nodules and the diagnosis of the nodules as benign or malignant. CAD systems for lung cancer are examined in a huge number of research case studies. CAD system steps and outlining of inhibitor genes at molecular level is being discussed. An insight into multi-omics and molecular dynamics simulations is also given in this paper.

Method for Calculating the Shape of Rolls for Helical Rolling

The paper is devoted to solving the problem of determining the shape of the rolls of helical rolling mills, depending on the specified profile of the deformation zone. A universal calculation method has been proposed, thanks to which it is possible to determine the shape of the working surface of a roll for all types of helical rolling mills (with mushroom-shaped, cup-shaped, barrel-shaped and disc rolls), any relative arrangement of the rolling axis and rolls axes, and various locations of the deformation zone on the rolling axis. The proposed method is implemented as a standalone exe-application with a simple intuitive interface. The application allows you to output the calculation results into txt-files, which can then be imported into CAD systems to create 3D roll models.

Toward a Multimodal Computer-Aided Diagnostic Tool for Alzheimer’s Disease Conversion

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder. It is one of the leading sources of morbidity and mortality in the aging population AD cardinal symptoms include memory and executive function impairment that profoundly alters a patient’s ability to perform activities of daily living. People with mild cognitive impairment (MCI) exhibit many of the early clinical symptoms of patients with AD and have a high chance of converting to AD in their lifetime. Diagnostic criteria rely on clinical assessment and brain magnetic resonance imaging (MRI). Many groups are working to help automate this process to improve the clinical workflow. Current computational approaches are focused on predicting whether or not a subject with MCI will convert to AD in the future. To our knowledge, limited attention has been given to the development of automated computer-assisted diagnosis (CAD) systems able to provide an AD conversion diagnosis in MCI patient cohorts followed longitudinally. This is important as these CAD systems could be used by primary care providers to monitor patients with MCI. The method outlined in this paper addresses this gap and presents a computationally efficient pre-processing and prediction pipeline, and is designed for recognizing patterns associated with AD conversion. We propose a new approach that leverages longitudinal data that can be easily acquired in a clinical setting (e.g., T1-weighted magnetic resonance images, cognitive tests, and demographic information) to identify the AD conversion point in MCI subjects with AUC = 84.7. In contrast, cognitive tests and demographics alone achieved AUC = 80.6, a statistically significant difference (n = 669, p < 0.05). We designed a convolutional neural network that is computationally efficient and requires only linear registration between imaging time points. The model architecture combines Attention and Inception architectures while utilizing both cross-sectional and longitudinal imaging and clinical information. Additionally, the top brain regions and clinical features that drove the model’s decision were investigated. These included the thalamus, caudate, planum temporale, and the Rey Auditory Verbal Learning Test. We believe our method could be easily translated into the healthcare setting as an objective AD diagnostic tool for patients with MCI.

SA-GAN: Stain Acclimation Generative Adversarial Network for Histopathology Image Analysis

Histopathological image analysis is an examination of tissue under a light microscope for cancerous disease diagnosis. Computer-assisted diagnosis (CAD) systems work well by diagnosing cancer from histopathology images. However, stain variability in histopathology images is inevitable due to the use of different staining processes, operator ability, and scanner specifications. These stain variations present in histopathology images affect the accuracy of the CAD systems. Various stain normalization techniques have been developed to cope with inter-variability issues, allowing standardizing the appearance of images. However, in stain normalization, these methods rely on the single reference image rather than incorporate color distributions of the entire dataset. In this paper, we design a novel machine learning-based model that takes advantage of whole dataset distributions as well as color statistics of a single target image instead of relying only on a single target image. The proposed deep model, called stain acclimation generative adversarial network (SA-GAN), consists of one generator and two discriminators. The generator maps the input images from the source domain to the target domain. Among discriminators, the first discriminator forces the generated images to maintain the color patterns as of target domain. While second discriminator forces the generated images to preserve the structure contents as of source domain. The proposed model is trained using a color attribute metric, extracted from a selected template image. Therefore, the designed model not only learns dataset-specific staining properties but also image-specific textural contents. Evaluated results on four different histopathology datasets show the efficacy of SA-GAN to acclimate stain contents and enhance the quality of normalization by obtaining the highest values of performance metrics. Additionally, the proposed method is also evaluated for multiclass cancer type classification task, showing a 6.9% improvement in accuracy on ICIAR 2018 hidden test data.

APPLICATION OF CAD IN PRINTED ELECTRONICS

The article discusses the use of CAD systems for printed electronics methods. The differences in the use of CAD for printed electronics in comparison with traditional ones are described. Arguments are given in favor of the need to create an applied CAD system to speed up the development and design process in printed electronics.

DESIGNING THE CONTENT OF GRAPHIC KNOWLEDGE IN THE CAD SYSTEMS ENVIRONMENT

The information support of the product life’s cycle requires to produce and to design this product with the help of the modern graphic CAD packages by creating an information model. In this regard, the question of the introduction of information technology in the educational process of the graphic training becomes relevant, as the basic graphic training forms the skills of graphic presentation of information. The purpose of the article is to substantiate the content of graphic training in institutions of higher technical education in conjunction with special software CAD-systems, including SolidWorks. The purpose of the study is to substantiate the content of graphic training in institutions of higher technical education in conjunction with the special software CAD-systems, including the SolidWorks. The methods of the research: analysis of the problems of future bachelors-mechanics’ graphic training, analysis of modern software for the graphic documentation, the generalization to obtain the conclusions and recommendations. The research results demonstrate that the development of modern software to solve problems of automation of three-dimensional design, the design and technological preparation of production of any complexity in various industries allows graduates to be competitive professionally in the labor market. We considered the graphic training in the environment of CAD-systems, also we highlighted the features of a special software SolidWorks-CAD. In the course of research of the content of basic graphic training that includes the descriptive geometry, the engineering and computer graphics in CAD-systems they revealed the most important factors in the formation of methods of mental activity of students. We consider that the most progressive method of the graphic training of engineers is to teach two courses instead of the three classic parts of basic graphic training. The effected research of engineering graphics and the peculiarities of the construction of working drawings in SolidWorks allowed to reconcile their relationship. The mastering of the modern software for solving of the problems of automation of three-dimensional design, the design and technological preparation of production of any complexity in various industries allows for graduates to be competitive professionally in the labor market.

Semantic and Content-Based Medical Image Retrieval with Proven Pathology for Lung Cancer Diagnosis

In lung cancer computer-aided diagnosis (CAD) systems, having an accurate ground truth is critical and time consuming. Due to lack of ground truth and semantic information, lung CAD systems are not progressing in the manner these are supposed to. In this study, we have explored Lung Image Database Consortium (LIDC) database containing annotated pulmonary computed tomography (CT) scans, and we have used semantic and content-based image retrieval (CBIR) approach to exploit the limited amount of diagnostically labeled data in order to annotate unlabeled images with diagnoses. We evaluated the method by various combinations of lung nodule sets as queries and retrieves similar nodules from the diagnostically labeled dataset. In calculating the precision of this system Diagnosed dataset and computer-predicted malignancy data are used as ground truth for the undiagnosed query nodules. Our results indicate that CBIR expansion is an effective method for labeling undiagnosed images in order to improve the performance of CAD systems while tested on PGIMER data. Also a little knowledge of biopsy confirmed cases can also assist the physician’s as second opinion to mark the undiagnosed cases and avoid unnecessary biopsies

Computer-Aided Detection of Human Lung Nodules on Computer Tomography Images via Novel Optimized Techniques

Background: Globally, the most general reason for huge number of passings is Lung disease. The lung malignancy is the most shocking amongst the tumor types and it plays a significant role for the increase of death rate. It is assessed that nearly 1.2 million persons are determined to have this illness and about 1.1 million individuals are losing their lives due to this sickness in every year. The survival rate is superior if the growth is recognized at earlier periods. The premature identification of lung malignant growth isn't a simple task. Various imaging algorithms are available for detecting the lung cancer. Aim: Computer aided diagnosis scheme is more useful for radiologist in detecting and identifying irregularities in advance and more rapidly. The CAD systems usually focus on identifying and detecting the lung nodules. Staging the lung cancer at its detection need to be focused as the treatment is based on the stage of the cancer. The major drawbacks of existing CAD systems are less accuracy in segmenting the nodule and staging the lung cancer. Objective: The most important intention of this work is to divide the lung nodule from CT image and classify as tumorous cells in order to identify the cancer's position with greater sensitivity, precision, and accuracy than other strategies. Methods: The primary role is defined as follows (i) for de-noising and edge sharpening of lung image, the curvelet transform is used. (ii) The Fuzzy thresholding technique is used to perform lung image binarization and lung boundary corrections. (iii) Segmentation is performed by using K-means algorithm. (iv) By using convolutional neural network (CNN), different stages of lung nodules such as benign and malignant are identified. Results: The proposed classifier achieves a 97.3 percent accuracy. The proposed approach is helpful in detecting lung cancer in its early stages. The proposed classifier achieved a sensitivity of 98.6 percent and a specificity of 96.1 percent. Conclusion: The results demonstrated that the established algorithms can be used to assist a radiologist in classifying lung images into various stages, thus supporting the radiologist in decision making.

Comparing the use of modern and traditional building design systems in construction projects

Any construction project through several stages, starting from the feasibilitystudy stage to the project delivery stage, as the most critical stage that must be focused on is the design stage. It was essential to adopt modern and advanced programs at the design stage to avoid problems and claims between the employer and the contractor due to changes and errors that occur in the project—design process. BIM is one of the modern technologies with many benefits that can be used to solve these errors and obstacles. This research aims to clarify the effectiveness of introducing the BIM system in the design stage of the construction building by comparing it with traditional CAD systems. In this research, ten different actual projects were studied in the architectural design stage to be used in the comparison process between BIM and CAD systems. The researcher reached a set of results, including that introducing the (BIM) in the architectural and structural design stage can reduce the time required to produce design documents by up to (67.6%). This study is helpful for all governmental and private design companies and encourages decision-makers to use the BIM system.