Real Images Research Articles

Early Childhood Care and Education: A Systematic Literature Review in Indian Context

Early Childhood Care and Education (ECCE) is often regarded as A key element of universal education. This study delves into the landscape of Early Childhood Care and Education (ECCE) in India through a systematic review of literature. After extensive attempts, several nations throughout the world are making strides toward securing ECCE. Despite this, the majority of them were unable to reach or compromise on this aim owing to insufficient resources, bad planning, and weak strategies for implementation, particularly in a large and populated nation such as India. The development of quality standards and curriculum frameworks, plans, acts, constitutional amendments, schemes, and flagship programmes like, District Primary Education Programme (DPEP), Integrated Child Development Services (ICDS) and Sarva Shiksha Abhiyan are just a few of the initiatives the Indian government has launched to improve quality. It has resulted in both anticipated and unexpected concerns and obstacles due to India's compromise on the non- negotiable ECCE quality requirements. Consequently, the government's objective of guaranteeing quality in ECCE was not fully achieved. However, recent research indicates significant advancements in quality improvements. Of course, there are unsatisfactory results in certain areas and components, but there has also been progress in other areas, suggesting that maintaining quality is a realistic objective. This research aims to present an overview of the Government of India's operations, assess the situation on the ground, pinpoint the primary hurdles to ECCE quality development and offer workable solutions to overcome those hurdles.

The value attitude of a person to their psychological health: approaches to the definition

The article is devoted to identification of the main characteristics that constitute the concept “value attitude of a person to their psychological health” that was conducted on the basis of the analysis of domestic and foreign literature. Firstly, the characteristic “health” was highlighted which is a state of the body characterised by internal comfort providing various functions of vital activity, creating mental, social and physical well-being that forms the basis of life quality. Secondly, the authors defined the characteristic “psychological health” as a psychological state of internal well-being of a person which depends on the individual-personal characteristics of a human, on their views and attitudes, on the degree of satisfaction of their needs and implementation of their own life plans. It is also seen as the ability of a person to withstand various stresses, changes in life and the ability to recover after that. Thirdly, the characteristic “value” was outlined. It is ideological orientation of the individual which is formed by their ideas of ideals, moral standards of behaviour in everyday life and which determines the personal meaning. Fourthly, the characteristic “attitude” was also studied. It is a conscious, selective, experience-based psychological connection of a person with reality which is expressed in actions, reactions and experiences and is formed in the process of activity. The final characteristic of the considered concept was the “value attitude”. It is defined as a one-sided relationship between a subject and an object when the subject assess the objects of reality according to their certain moral criteria. Thus, on the basis of the analysis of domestic and foreign psychological and pedagogical approaches, the authors have formulated the definition of the concept “value attitude of a person to their psychological health”.

Can Generative AI improve social science?

Generative AI that can produce realistic text, images, and other human-like outputs is currently transforming many different industries. Yet it is not yet known how such tools might influence social science research. I argue Generative AI has the potential to improve survey research, online experiments, automated content analyses, agent-based models, and other techniques commonly used to study human behavior. In the second section of this article, I discuss the many limitations of Generative. I examine how bias in the data used to train these tools can negatively impact social science research—as well as a range of other challenges related to ethics, replication, environmental impact, and the proliferation of low-quality research. I conclude by arguing that social scientists can address many of these limitations by creating open-source infrastructure for research on human behavior. Such infrastructure is not only necessary to ensure broad access to high-quality research tools, I argue, but also because the progress of AI will require deeper understanding of the social forces that guide human behavior.

STABLE DIFFUSION TEXT TO IMAGE USING AI

The Stable Diffusion Text-to-Image Generation Project is an innovative endeavor in the field of generative adversarial networks (GANs) and natural language processing (NLP). This project aims to bridge the semantic gap between textual descriptions and visual content by utilizing the Stable Diffusion training framework to generate highly realistic and coherent images from text prompts. The project leverages recent advancements in deep learning techniques to tackle the challenging task of text-to image synthesis. The project introduces an innovative approach at the crossroads of generative adversarial networks (GANs) and natural language processing (NLP). It aims to bridge the semantic gap between textual descriptions and visual content by utilizing the Stable Diffusion training framework. The key goal is to generate highly realistic and coherent images from text prompts, leveraging recent deep learning Stable Diffusion. The Stable Diffusion training framework plays a central role in this project. It’s a sophisticated training methodology for GANs, designed to stabilize the training process. GANs have exhibited great potential in generating images but often suffer from issues like mode collapse and training instability. Keywords: Stable Diffusion, Text-to-Image Generation, Image Synthesis, Natural Language Processing(NLP), Generative Adversarial Networks (GANs)

Arm Robot using Machine Learning

As human civilization is advancing the usage of robots becoming very common. Robots plays a important role in industrialization and human advancement. Arm robot are one of the most popular robots which are used in industries. These robots are most efficient for manufacturing process. In our robot we have integrated with Arduino micro controller and a esp32 cam module which results in less capital investment for the robot. The esp32 cam enables the robot with real time object detection and the Arduino micro controller is used for robot moment manipulation. The body the robot is made using 3D printed parts. Key Words: Arm robot, Arduino micro controller, ESP32Cam module, Object detection, 3D printing.

Constructing immune and prognostic features associated with ADCP in hepatocellular carcinoma and pan-cancer based on scRNA-seq and bulk RNA-seq

AimDespite the significant therapeutic outcomes achieved in systemic treatments for liver hepatocellular carcinoma (LIHC), it is an objective reality that only a low proportion of patients exhibit an improved objective response rate (ORR) to current immunotherapies. Antibody-dependent cellular phagocytosis (ADCP) immunotherapy is considered the new engine for precision immunotherapy. Based on this, we aim to develop an ADCP-based LIHC risk stratification system and screen for relevant targets.MethodUtilizing a combination of single-cell RNA sequencing (scRNA-seq) and bulk RNA-seq data, we screened for ADCP modulating factors in LIHC and identified differentially expressed genes along with their involved functional pathways. A risk scoring model was established by identifying ADCP-related genes with prognostic value through LASSO Cox regression analysis. The risk scoring model was then subjected to evaluations of immune infiltration and immunotherapy relevance, with pan-cancer analysis and in vitro experimental studies conducted on key targets.ResultsBuilding on the research by Kamber RA et al., we identified GYPA, CLDN18, and IRX5 as potential key target genes regulating ADCP in LIHC. These genes demonstrated significant correlations with immune infiltration cells, such as M1-type macrophages, and the effectiveness of immunotherapy in LIHC, as well as a close association with clinical pathological staging and patient prognosis. Pan-cancer analysis revealed that CLDN18 was prognostically and immunologically relevant across multiple types of cancer. Validation through tissue and cell samples confirmed that GYPA and CLDN18 were upregulated in liver cancer tissues and cells. Furthermore, in vitro knockdown of CLDN18 inhibited the malignancy capabilities of liver cancer cells.ConclusionWe have identified an ADCP signature in LIHC comprising three genes. Analysis based on a risk scoring model derived from these three genes, coupled with subsequent experimental validation, confirmed the pivotal role of M1-type macrophages in ADCP within LIHC, establishing CLDN18 as a critical ADCP regulatory target in LIHC.

TEXT TO IMAGE GENERATOR USING AI

This web application is proposed to generate images that is given in a prompt. This can generate imaginary pictures. For the conversion, we need DALL E & Open Ai. It will be fun creating the artistic, realistic images from the prompt. This is a Web Application project developed using A& OpenAI, we used Natural language description prompt for our project. It creates images through prompts. This will be advantage for executing different ideas, thoughts into textual presentation. DALLE can be used for advertising, printing, selling etc. Using this web application project, we can enhance our imaginative ideas into a realistic one. It is a friendly web where we won't face any issues in pictures. And We can't find this imaginary picture generator in any search engine. This Android application project will give you a picture with whatever size you want. And it won't reduce the quality of a picture. The quality size of a picture will be 256 × 256, 512 × 512 and 1024 × 1024.We can choose a quality size based on our network quality. Before using this web application, we have to make sure the network facilities. Keywords – Web application, user login, user registration, prompt field, search field, download, image assessment.

Visualisation in architecture – opportunities and threats

Development of computer technology, computing power of graphics cards and processors allows, with the use of appropriate software, to create extremely realistic three-dimensional images. The effects of generated visualisations can be difficult to distinguish from real photos of objects. Architects often verify the fulfilment of design goals based on a rendered digital image. Unfortunately, designers are often not fully aware of the limitations and simplifications of the tools used to create visualisations. An uncritical approach to the presented results may lead to design errors. This article presents the possibilities of visualisation in selected computer programs used by architects and cases in which correct visualisation is possible only with the use of specialised software. The article pays special attention to the possibility of errors in the visualisation of architectural lighting.

Enhancing X-ray Security Image Synthesis: Advanced Generative Models and Innovative Data Augmentation Techniques

This study addresses the field of X-ray security screening and focuses on synthesising realistic X-ray images using advanced generative models. Insufficient training data in this area pose a major challenge, which we address through innovative data augmentation techniques. We utilise the power of generative adversarial networks (GANs) and conditional GANs (cGANs), in particular the Pix2Pix and Pix2PixHD models, to investigate the generation of X-ray images from various inputs such as masks and edges. Our experiments conducted on a Korean dataset containing dangerous objects relevant to security screening show the effectiveness of these models in improving the quality and realism of image synthesis. Quantitative evaluations based on metrics such as PSNR, SSIM, LPIPS, FID, and FSIM, with scores of 19.93, 0.71, 0.12, 29.36, and 0.54, respectively, show the superiority of our strategy, especially when integrated with hybrid inputs containing both edges and masks. Overall, our results highlight the potential of advanced generative models to overcome the challenges of data scarcity in X-ray security screening and pave the way for more efficient and accurate inspection systems.

Design of masonry strengthening with one-sided shotcrete applications

Load-bearing masonry is quite widespread, including in earthquake-prone areas and zones in which military operations were carried out. At the same time, the existing treasure models, as well as reinforcement technologies, are mainly focused either on strengthening in order to increase the load-bearing capacity, or on restoring structures damaged during operation, actually static loads. Complex retrofitting methods, as well as models of reinforced masonry that have been damaged as a result of seismic or mine-explosive impacts, for the most part are not focused on mass use and imply mainly the re-laying of destroyed areas. At the same time, there is a need to adapt existing high-tech methods of strengthening masonry structures for mass use, taking into account the latest developments in the field of materials science. The most optimal variant of reinforcement, according to the authors, was the use of single-sided shotcrete applications. For this method, a model of reinforced masonry was defined and described in order to carry out a calculation justification and a comprehensive calculation and design methodology was developed, including for load-bearing multi-storey buildings structures. On the basis of laboratory studies, a constructive solution for fixing applications was justified only due to the forces of adhesion to the surface of the base masonry. The specified complex technique was adapted on real objects of multi-storey buildings with load-bearing masonry walls.

Кількісне оцінювання функціональної ефективності мережевих структур за аналізом чутливості оптимізаційної моделі до зовнішніх впливів

The real network organizational structure is constantly under the influence of external influences, which can cause unexpected consequences due to the cascading spread of relevant disturbances directed at critically important parameters. Parametric sensitivity analysis is the latest computational procedure, the results of which provide the researcher with an idea and quantitative assessment of the appropriate response of the structure in the form of an analytical platform as a composition and interaction of scenario analysis, cascade process development and optimization modeling technology. The main task is timely adaptation to expected changes in business analytics, oriented to optimal resources distribution. The generalized minimum cost problem is a typical example of widespread problems of optimal distribution and use of limited, in particular, energy resources, of any nature and purpose, using the scientific management and analysis of decisions, in its network model attention is paid to the potentials of nodes. The maximum flow problem is an example of the active and effective application of methods and models of flows optimization, where the processes and objects used a network organization, the energy systems and complexes investigated, where arc parameters are critical in its model. In the article, for these classic optimization problems, mathematical and spreadsheet models were built on concrete examples. Direct and dual mathematical programming problems were solved, the following stages were carried out - the organization of a computer experiment and the construction of an analytical platform for evaluating the behavior of the network structure under the influence of external influences and disturbances as their consequences. To take into account the specific conditions of real objects with a network structure at the stage of modification, the model supplemented with appropriate restrictions and correction of input data sets. The obtained results should be useful for planning and management personnel and top-management for discussion and decision-making regarding, for example, the optimal placement of energy equipment, the weighted distribution of energy flows in the "source-sink" system or the appointment of executors for work, automated project management using project networks, etc. Keywords: optimization modeling, maximum network flow, minimum cost flows, network organizational structures, decision making, spreadsheet modeling and analytics.

Saliency-Guided Point Cloud Compression for 3D Live Reconstruction

3D modeling and reconstruction are critical to creating immersive XR experiences, providing realistic virtual environments, objects, and interactions that increase user engagement and enable new forms of content manipulation. Today, 3D data can be easily captured using off-the-shelf, specialized headsets; very often, these tools provide real-time, albeit low-resolution, integration of continuously captured depth maps. This approach is generally suitable for basic AR and MR applications, where users can easily direct their attention to points of interest and benefit from a fully user-centric perspective. However, it proves to be less effective in more complex scenarios such as multi-user telepresence or telerobotics, where real-time transmission of local surroundings to remote users is essential. Two primary questions emerge: (i) what strategies are available for achieving real-time 3D reconstruction in such systems? and (ii) how can the effectiveness of real-time 3D reconstruction methods be assessed? This paper explores various approaches to the challenge of live 3D reconstruction from typical point cloud data. It first introduces some common data flow patterns that characterize virtual reality applications and shows that achieving high-speed data transmission and efficient data compression is critical to maintaining visual continuity and ensuring a satisfactory user experience. The paper thus introduces the concept of saliency-driven compression/reconstruction and compares it with alternative state-of-the-art approaches.

DEVELOPMENT OF CLASSIFIERS OF DEVIATIONS FROM THE REQUIREMENTS OF REGULATORY DOCUMENTS OF THE OIL AND GAS INDUSTRY AND COMPENSATORY MEASURES

The paper analyzes the problem of ensuring safety at various stages of the life cycle of main pipelines and steel vertical tanks in cases of deviations from the requirements of current design standards. To effectively solve this problem, independent and easily modifiable classifiers of deviations from the requirements of regulatory documents, as well as measures aimed at compensating for these deviations, have been developed. The faceted method was chosen as the classification method, which allows us to divide a large object of study into smaller characteristics independent of each other, which facilitates the decision-making process and allows us to take into account many aspects.The resulting classifiers are a tool for systematization of information obtained during the analysis and development of special technical conditions necessary to ensure the safety of main pipelines and steel vertical tanks. Due to their ability to be easily modified, they can be adapted to specific objects and conditions, which makes them a versatile and flexible tool for developing and making engineering decisions.An important aspect of the work is the practical testing of the developed classifiers by applying them to a real object of the oil and gas industry. This stage of testing demonstrated the high efficiency and practical applicability of the proposed classifiers, which indicates their potential aimed at improving the safety of oil and gas transportation and storage facilities, as well as products of their processing.In addition, the systematization of information on deviations from the requirements of regulatory documents and ways to compensate for them allows not only to increase the level of safety during the construction, reconstruction and operation of pipelines and reservoirs, but also to reduce the consequences of possible emergencies.The results obtained represent an important step in the development of an approach to ensuring the safety of main pipelines and steel vertical tanks, and also have both practical and scientific value, and can be recommended for use by specialists in the field of design, technosphere safety and the development of special technical conditions.

Abstract PO2-03-01: Personalized 3D tumor models drive shared decision-making in early breast cancer

Abstract Background: Decision-making for early (Stages 1-3) breast cancer (eBC) care is complex and highly personal, requiring discussions between patients and multiple physicians. Shared decision-making (SDM) is an accepted process that combines input from both patients and physicians, although its overall use is low due to a lack of time and incentives, complicated application techniques, and few communication tools. Reviews show that SDM results in “improved affective-cognitive outcomes” but evidence linking to patient outcomes is sparse. Consequently, patients often receive mixed messages and feel anxious when making treatment decisions. SimBioSys (SBS) developed the TumorSight(TM) platform for individualized treatment planning, coordinated care, and SDM. The first application on this platform, TumorSight Viz, rapidly builds 3D computational models from standard breast MRI data, and further integrates deep learning, genomic/genetic testing, metabolomics, and clinical and demographic data to tailor these models for each individual diagnosed with eBC. The platform can also transform acquired data (e.g., prone) to surgical position (e.g., supine) to create realistic images for precision surgical planning and tools for better SDM with eBC patients. We are assessing how surgeons and patient advocates (PA) perceive TumorSight, and how it may support better SDM and treatment implementation. Methods: SBS conducted an exploratory survey with 31 eBC surgeons to assess the utility of TumorSight Viz visualizations across a range of didactic and planning applications and to query preferences for future components like predicted response to chemotherapy and immunotherapy. Questions were developed with input from breast surgeon advisors. More exploration on TumorSight Viz utility is planned with semi-structured qualitative interviews to assess patient advocates’ perceptions of 3D visualizations, surgical planning, and ability to support decision making. PAs will represent geographic, experience, and race/ethnicity diversity. Questions to elicit patient needs, perception of SBS tools, and quality-of-life are being finalized with academic advisors. These will also be used to develop a future eBC patient survey. Results: The SBS surgeon survey revealed that 96% of breast surgeons find visualization important to surgical planning, and these depictions were considered to be critical by a third of breast surgeons surveyed. 96% also listed the conversion of prone to supine visualizations as important (45% critical, 35% valuable, 16% nice to have, 0% not important) for surgical planning value. Surgeons also assigned strong utility for: Graphical 3D rotation to determine tumor shape, size, location, positioning, and landmarks relative to other breast structures – 100% Ability to instantly provide tumor-to-breast volume determination with high accuracy – 96% Ability to instantly provide anticipated tumor extirpation volume to breast volume with high accuracy – 96% Automatic tumor-to-landmark structures measurements (nipple, chest, skin) – 96% Visualization of tumor in the context of other anatomical structures (e.g., skin, blood vessels, chest, heart) – 87% Initial PA discussions (n=3) listed the importance of individualized treatment based on evidence instead of hunches; reasons to avoid prophylactic mastectomies; and ways to lower patient anxiety. Conclusions: Survey-based feedback shows value to eBC surgeons and patients to coordinate care and personalize approaches for optimal outcomes. Computational tools can facilitate patient understanding and patient-tailored treatment. Additional surveys are planned with patients and eBC patient advocacy groups to assess the utility and potential expansion of patient-specific 3D tumor visualizations, both of which will be further discussed. Citation Format: Deborah Collyar, Matthew Biancalana, Anuja Antony, Nicole Liadis, Jyoti Palaniappan, Rebecca Seago-Coyle. Personalized 3D tumor models drive shared decision-making in early breast cancer [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO2-03-01.

Abstract PO1-07-06: Sonographic assessment of breast implants using strain elastography and shear wave elastography in an animal model

Abstract Purpose: To date, MRI remains the gold standard for the diagnosis of breast implant rupture. As MRI is an expensive procedure with limited availability, improvement in sonographic assessment is desirable. A potentially useful tool in this regard is the assessment of tissue stiffness using ultrasound elastography. Strain elastography provides a qualitative analysis by producing a coloured map in the B-mode image comparing the stiffness of adjacent tissues in the region of interest, whereas shear wave elastography provides a quantitative analysis. To evaluate the diagnostic utility of both methods under standardised conditions, we developed an animal model. Under natural conditions, the physiology of the patient (e.g. the amount and stiffness of the breast tissue covering the implant in question) can influence the measurements and alter the data. Therefore, the animal model provides an excellent opportunity to compare ruptured and intact implants of different types and sizes under exactly the same conditions, neglecting individual characteristics of real patients. Methods: An animal model was created by preparing an implant site in a chicken breast, mimicking the layers of tissue typically covering a breast implant after mastectomy (skin, fat, muscle). Different fractured and intact implants were inserted. Measurements were made using strain elastography and shear wave elastography by an investigator with no further experience of breast ultrasound. Implants were scanned in five pre-defined planes in both intact and ruptured conditions. For strain elastography, the resulting images were examined for repetitive patterns. Shear wave elastography data were analysed for significant differences between ruptured and intact implants. Results: The chicken breast animal model produced realistic images and measurements comparable to those of a human breast. Therefore, ruptured and intact implants could be compared under standardised conditions. Statistical analysis showed no significant difference between intact and ruptured implants in shear wave elastography data. However, qualitative analysis using strain wave elastography showed different patterns when comparing intact and ruptured implants in the animal model. Intact implants showed a characteristic three-layer sonographic image at certain levels. Conclusion: Shear wave elastography does not appear to provide reliable data for the evaluation of breast implants, whereas qualitative analysis using strain wave elastography may be a useful tool to improve diagnostic accuracy. Citation Format: Hanna Fritsch, Melisa Celik, Matthias Warm, Fabinshy Thangarajah, Anett Pisek, Christian Eichler. Sonographic assessment of breast implants using strain elastography and shear wave elastography in an animal model [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO1-07-06.

A novel experimental test of truthful bidding in second-price auctions with real objects

We present experimental evidence on bidding in second-price auctions with real objects. Our novel design, combining a second-price auction with an individual-specific binary-choice task based on the outcome of the auction, allows us to directly identify over- and under-bidding. We analyze bidding in real-object and induced-value auctions, and find significant deviations from truthful bidding in both. Overall, under-bidding is somewhat more prevalent than over-bidding; yet, the latter has a bigger magnitude, especially with induced values. At the individual level, however, we find that participants who over-bid in induced-value auctions are equally likely to over- or under-bid in real-object ones; moreover, there is no association in the size of the deviations from truthful bidding across the two types of auctions. Hence, we conclude that there is no relation between the tendency to deviate from truthful bidding in induced-value vs. real-object auctions.

Multi-stage generative adversarial networks for generating pavement crack images

The application of machine learning techniques in pavement health monitoring based on computer vision has greatly improved the accuracy and efficiency in the detection of pavement distress levels and categories. However, a persistent challenge in this field is the issue of sample imbalance, primarily arising from the scarcity of cracked pavement images, which hampers their effectiveness in road maintenance engineering. To address this issue and enhance the fast and stable generation of high-quality crack images for engineering purposes, this study proposes two frameworks based on Generative Adversarial Networks (GAN): Multi-Stage GAN-v1 and Multi-Stage GAN-v2. These frameworks break down the complex task of directly generating high-quality images into a series of incremental steps, gradually increasing the image resolution from initially generated low-precision images. Both versions, v1 and v2, consist of multiple sequentially connected generation units, with each unit utilizing the Wasserstein Generative Adversarial Network-Gradient Penalty (WGAN-GP). Furthermore, v2 has the additional capability of generating pavement crack images of specified types and simultaneously providing crack segmentation labels. This feature significantly enhances the practical applicability of the generated data in engineering contexts. In a comprehensive case study, the evaluation results clearly illustrate the superior image generation quality from the two proposed frameworks. Moreover, the results from ablation experiments, involving the training of nine state-of-the-art crack semantic segmentation and object detection networks using both generated images and real images, demonstrate the effective utility of these generated images for training pavement distress detection networks.

Neural networks for intelligent multilevel control of artificial and natural objects based on data fusion: A survey

Today the tasks of complex artificial and natural objects control have come to the fore in the majority of subject domains. The efficiency and effectiveness of solving these tasks directly depends of the efficiency and effectiveness of data fusion (DF). Data fusion methods are designed to integrate data from multiple sources and transform it in order to produce more consistent, accurate, and useful information than that provided by any individual data source. Although DF has been extensively studied for a considerable period of time it is still hardly applicable in practice in the processes of the control of the real world objects with complex structure and behavior as the data produced by the objects is, in the majority of cases, heterogeneous, multimodal, and imperfect, has huge volume. To ensure proper response to the changes in the state and behavior of the controlled objects that can be caused by both internal and external influencing factors the data should be processed with high accuracy and with minimum delays. Despite the importance of the tasks of complex objects control till now there are no researches that clarify to what extent the DF problem has been solved from the perspective of its application in the processes of objects control based on the data received from the objects. In the survey we define the requirements to DF in the interests of the control of complex artificial and natural objects, consider the structure of the multilevel process of intelligent object control, identify the neural networks that can be used in the control process for data fusion. Despite the wide capabilities of the existing NN we reveal that they still do not meet all the requirements to DF for complex objects control. Based on the analysis of NN architectures, we define requirements for advanced NN architectures and discuss future research directions. To facilitate our literature analyses, we also perform conceptual exploration of collected papers with lattices of closed itemsets and implications from Formal Concept Analysis and Data Mining used for knowledge processing in similar large-scale studies.

A novel approach for melanoma detection utilizing GAN synthesis and vision transformer

Background and objective:Melanoma, a malignant form of skin cancer, is a critical health concern worldwide. Early and accurate detection plays a pivotal role in improving patient’s conditions. Current diagnosis of skin cancer largely relies on visual inspections such as dermoscopy examinations, clinical screening and histopathological examinations. However, these approaches are characterized by low efficiency, high costs, and a lack of guaranteed accuracy. Consequently, deep learning based techniques have emerged in the field of melanoma detection, successfully aiding in improving the accuracy of diagnosis. However, the high similarity between benign and malignant melanomas, combined with the class imbalance issue in skin lesion datasets, present a significant challenge in further improving the diagnosis accuracy. We propose a two-stage framework for melanoma detection to address these issues. Methods:In the first stage, we use Style Generative Adversarial Networks with Adaptive discriminator augmentation synthesis to generate realistic and diverse melanoma images, which are then combined with the original dataset to create an augmented dataset. In the second stage, we utilize a vision Transformer of BatchFormer to extract features and detect melanoma or non-melanoma skin lesions on the augmented dataset obtained in the previous step, specifically, we employed a dual-branch training strategy in this process. Results:Our experimental results on the ISIC2020 dataset demonstrate the effectiveness of the proposed approach, showing a significant improvement in melanoma detection. The method achieved an accuracy of 98.43%, an AUC value of 98.63%, and an F1 value of 99.01%, surpassing some existing methods. Conclusion:The method is feasible, efficient, and achieves early melanoma screening. It significantly enhances detection accuracy and can assist physicians in diagnosis to a great extent.

In-Domain GAN Inversion for Faithful Reconstruction and Editability.

Generative Adversarial Networks (GANs) have significantly advanced image synthesis through mapping randomly sampled latent codes to high-fidelity synthesized images. However, applying well-trained GANs to real image editing remains challenging. A common solution is to find an approximate latent code that can adequately recover the input image to edit, which is also known as GAN inversion. To invert a GAN model, prior works typically focus on reconstructing the target image at the pixel level, yet few studies are conducted on whether the inverted result can well support manipulation at the semantic level. This work fills in this gap by proposing in-domain GAN inversion, which consists of a domain-guided encoder and a domain-regularized optimizer, to regularize the inverted code in the native latent space of the pre-trained GAN model. In this way, we manage to sufficiently reuse the knowledge learned by GANs for image reconstruction, facilitating a wide range of editing applications without any retraining. We further make comprehensive analyses on the effects of the encoder structure, the starting inversion point, as well as the inversion parameter space, and observe the trade-off between the reconstruction quality and the editing property. Such a trade-off sheds light on how a GAN model represents an image with various semantics encoded in the learned latent distribution.