Microsatellite instability (MSI) is a pivotal genetic marker influencing the efficacy of immunotherapy in colorectal cancer. Traditional MSI examination often requires additional genetic or immunohistochemical tests, ...
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A vast number of technologies based on Artificial Intelligence (AI) have proliferated into various application domains. As part of its objectives to develop agents which can behave and think like humans, some branches...
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The problems of Electromagnetic Compatibility (EMC) in three-phase systems are less studied than single-phase or direct current, due to the smaller ratio of emissions relative to the rated power of such systems. This ...
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In our study, we explore methods for detecting unwanted content lurking in visual datasets. We provide a theoretical analysis demonstrating that a model capable of successfully partitioning visual data can be obtained...
ISBN:
(纸本)9798331314385
In our study, we explore methods for detecting unwanted content lurking in visual datasets. We provide a theoretical analysis demonstrating that a model capable of successfully partitioning visual data can be obtained using only textual data. Based on the analysis, we propose Hassle-Free Textual Training (HFTT), a streamlined method capable of acquiring detectors for unwanted visual content, using only synthetic textual data in conjunction with pre-trained vision-language models. HFTT features an innovative objective function that significantly reduces the necessity for human involvement in data annotation. Furthermore, HFTT employs a clever textual data synthesis method, effectively emulating the integration of unknown visual data distribution into the training process at no extra cost. The unique characteristics of HFTT extend its utility beyond traditional out-of-distribution detection, making it applicable to tasks that address more abstract concepts. We complement our analyses with experiments in out-of-distribution detection and hateful image detection. Our codes are available at https://***/Saehyung-Lee/HFTT
Opinion has always affected businesses and individuals especially from the Public. People react through social media and spread it incompletely. The situation was then accepted as public opinion. There are three categ...
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Design practices and tools for human exploration missions have evolved in concert with mission complexity over the past half century of the space age. As collective thought turns toward the exploration of Mars and of ...
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Depressive Disorders (DD) is one of the most prevalent mental disorders in the world that may lead to suicide cases. To prevent the latter, ubiquitous early detection systems may be effective. Recent studies have sinc...
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We reviewed the application of modern technology for rapid and accurate multi-person real-time pose detection in the hazardous field of electricalengineering. We focused on two leading pose detection technologies: YO...
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This article examines the incorporation of the Shopping Assistance Automatic Suggestion (SAAS) model into Virtual Reality (VR) environments in order to improve the online shopping experience. The SAAS model employs so...
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Hepatocellular carcinoma (HCC), – the main form of liver cancer –, is the second global leading cause of cancer-related mortality. LI-RADS is considered the worldwide non-invasive standard method for imaging interpr...
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ISBN:
(纸本)9783031783975
Hepatocellular carcinoma (HCC), – the main form of liver cancer –, is the second global leading cause of cancer-related mortality. LI-RADS is considered the worldwide non-invasive standard method for imaging interpretation and reporting in patients with HCC eliminating the need for biopsy. However, it might be prone to interpretation subjectivity. Therefore, we develop an objective non-invasive AI-based grading system for HCC for appropriate etiology treatment plans. The developed system integrates potential image-based markers that represent the tumor’s morphology, functionality, and appearance/texture with the associated clinical biomarkers. The study encompasses 117 patients diagnosed with HCC and was divided into three different groups (group 1: benign low-grade (LR 1,2), N = 41;group 2: malignant high-grade (LR 4,5), N = 39;and group 3: malignant not HCC (LR-M), N = 37). Diffusion-weighted magnetic resonance imaging (DWI) was acquired for imaging-based markers identification. The developed grading system pipeline includes: i) estimation of morphological markers using a new parametric spherical harmonic model, ii) estimation of appearance/textural markers using a novel rotation invariant circular binary pattern model, iii) calculation of the functional markers by constructing the representative cumulative distribution functions of the estimated apparent diffusion coefficients, and iv) integrating the aforementioned imaging-based markers with the associated clinical biomarkers, known as Alpha-fetoprotein. The integrated markers were optimized to train and test multiple machine learning (ML) classifiers and a hyper-tuned custom CNN. On a randomly stratified train (80%) test (20%) split scheme, the developed obtained an overall accuracy of 88% in differentiating between the three groups using the integrated markers along with the CatBoost classifier, surpassing the diagnostic performance of individual marker sets, other ML classifiers, and the CNN as well. The obta
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