BACKGROUND: Current automated cervical cytology screening systems still heavily depend on manipulation of glass slides. We developed a new system called CytoProcessorTM (DATEXIM, Caen, France), which increases sensitivity and takes advantage of virtu...
Ovarian cancer is a very insidious malignant tumor. In order to detect ovarian cancer cells early, the classification and recognition of ovarian cancer cells is mainly studied by two-dimensional light scattering technology. Firstly, a single-cell two...
OBJECTIVES: To retrospectively evaluate the diagnostic performance of a convolutional neural network (CNN) model in detecting pneumothorax on chest radiographs obtained after percutaneous transthoracic needle biopsy (PTNB) for pulmonary lesions.
In this paper the EEG signal is analyzed by reconstructing the time series EEG signal in High dimensional Phase Space. The computational complexity in higher dimension is reduced by Principal Component Analysis for the High dimensional Phase Space ou...
Coronary calcium detection in medicine image processing is a hot research topic. According to the low resolution and complex background in medicine image, an improved coronary calcium detection algorithm based on the Single Shot MultiBox Detector (SS...
The application of deep learning to neuroimaging big data will help develop computer-aided diagnosis of neurological diseases. Pattern recognition using deep learning can extract features of neuroimaging signals unique to various neurological disease...
OBJECTIVE: We aim to study to what extent conventional and deep-learning-based T relaxometry patterns are able to distinguish between knees with and without radiographic osteoarthritis (OA).
Computer aided diagnosis using artificial intelligent techniques made tremendous improvement in medical applications especially for easy detection of tumor area, tumor type and grades. This paper presents automatic glioma tumor grade identification f...
PURPOSE: We aimed to use deep learning with convolutional neural network (CNN) to discriminate between benign and malignant breast mass images from ultrasound.
BACKGROUND: Following visible successes on a wide range of predictive tasks, machine learning techniques are attracting substantial interest from medical researchers and clinicians. We address the need for capacity development in this area by providi...
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