Artificial intelligence (AI) is showing promise in improving clinical diagnosis. In breast cancer screening, recent studies show that AI has the potential to improve early cancer diagnosis and reduce unnecessary workup. As the number of proposed models and their complexity grows, it is becoming increasingly difficult to re-implement them. To …
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WhatsApp: +86 18221755073A classifier combining chest X-ray (CXR) with clinical features may serve as a rapid screening approach. Methods: The study included 512 patients with COVID-19 and 106 with influenza A/B pneumonia. A deep neural network (DNN) was applied, and deep features derived from CXR and clinical findings formed fused features for diagnosis …
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WhatsApp: +86 18221755073With a screen vibration frequency of 30 Hz and an average contact time on the screen surface of circa 3 seconds, the material being screened undergoes about 100 …
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WhatsApp: +86 18221755073Screening classifiers are increasingly used to identify qualified candidates in a variety of selection processes. In this context, it has been recently shown that, if a classifier is calibrated, one can identify the smallest set of candidates which contains, in expectation, a desired number of qualified candidates using a threshold decision rule. …
WhatsApp: +86 18221755073A shortcoming of a drum loose materials size classifier is its low classification efficiency due to formation of a segregation core around the circulation center. A screen design that allows ...
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WhatsApp: +86 18221755073Among the non-invasive Colorectal cancer (CRC) screening approaches, Computed Tomography Colonography (CTC) and Virtual Colonoscopy (VC), are much more accurate. This work proposes an AI-based polyp detection framework for virtual colonoscopy (VC). Two main steps are addressed in this work: automatic segmentation …
WhatsApp: +86 18221755073Hash Transformation and Machine Learning-Based Decision-Making Classifier Improved the Accuracy Rate of Automated Parkinson's Disease Screening Abstract: Digitalized hand-drawn pattern is a noninvasive and reproducible assistive manner to obtain hand actions and motions for evaluating functional tremors and upper-limb movement disorders.
WhatsApp: +86 18221755073Breast cancer is the most common cancer among women in Taiwan, and the number of breast cancer cases reported annually continues to increase. In 2018, breast cancer ranked fourth in terms of mortality. Early stages (stages 0-2) of malignant breast lesions can be diagnosed during regular screening, and early treatment via advanced …
WhatsApp: +86 18221755073Screening mammography, a low-dose X-ray examination, is a major tool for early detection of breast cancer. A standard screening mammogram consists of two high-resolution X-rays of each breast, taken from the side (the "mediolateral" or MLO view) and from above (the "craniocaudal" or CC view) for a total of four images.
WhatsApp: +86 18221755073DOI: 10.1016/b978-0-12-822822-7.00011-9 Corpus ID: 237957915; A deep learning predictive classifier for autism screening and diagnosis @article{Wang2021ADL, title={A deep learning predictive classifier for autism screening and diagnosis}, author={Haishuai Wang and Lianhua Chi and Hong Yang and Li Li and Ziping Zhao}, journal={Neural …
WhatsApp: +86 18221755073In [10], it is suggested that the service life of ballast layer at different classes (grading) can be estimated by the abrasion number (AN) of the ballast material, as shown in Fig. 2.How to calculate AN is explained in Table 6. Fig. 2 also shows that there is a significant difference in the service life of ballast materials. It is easy to see that when …
WhatsApp: +86 18221755073A classifier combining chest X-ray (CXR) with clinical features may serve as a rapid screening approach. Methods: The study included 512 patients with COVID-19 and 106 with influenza A/B pneumonia.
WhatsApp: +86 18221755073Important features from models. All of the classifiers in the study could show important features in each model. For the classifier using decision tree, BMI, the level of TG and ALT, and the ratio ...
WhatsApp: +86 18221755073Acoustic Analysis of Speech for Screening for Suicide Risk: Machine Learning Classifiers for Between- and Within-Person Evaluation of Suicidality J Med Internet Res. 2023 Mar 23:25: e45456. doi ... The between-person classifier was able to detect high suicidality with 69% accuracy (sensitivity 74%, specificity 62%, area under …
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WhatsApp: +86 18221755073On the NYU Breast Cancer Screening Dataset, consisting of more than one million images, our model achieves an AUC of 0.93 in classifying breasts with malignant findings, outperforming ResNet-34 ...
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WhatsApp: +86 18221755073The screening method integrating the astable multivibrator model and the machine learning classifier was designed in an embedded system using the 'Timed Loop' and the 'Stacked Sequence Structure' [21, 30], including logics of data acquisition, astable multivibrator, and machine learning classifier.
WhatsApp: +86 18221755073Globally-Aware Multiple Instance Classifier for Breast Cancer Screening Mach Learn Med Imaging. 2019 Oct:11861:18-26. doi: 10.1007/978-3-030-32692-0_3. ... model outperforms the ResNet-based baseline and achieves radiologist-level performance in the interpretation of screening mammography. Although our model is trained only with …
WhatsApp: +86 182217550731. Introduction. As the second leading cause of cancer death among women in the US, breast cancer has been studied for decades. While studies have shown screening mammography has significantly reduced breast cancer mortality, it is an imperfect tool [].To address its limitations, convolutional neural networks (CNN) designed for computer …
WhatsApp: +86 18221755073fdepend on the classifier and data distribu-tion. These results lend support to focusing on calibration as the only requirement for screening classifiers. In this work, we argue that screening policies given by threshold decision rules using calibrated classifiers may suffer from an under-studied type of unfairness—they may be biased against ...
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