The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
The fovea is one of the most important anatomical landmarks in the eye and its localization is required in automated analysis of retinal diseases due to its role in sharp central vision. In this paper, we propose a two-stage deep learning framework for accurate segmentation of the fovea in retinal colour fundus images. In the first stage, coarse segmentation is performed to localize the fovea in the...
The segmentation of media-adventitia and lumen-intima boundaries of the Carotid Artery forms an essential part in assessing plaque morphology in Ultrasound Imaging. Manual methods are tedious and prone to variability and thus, developing automated segmentation algorithms is preferable. In this paper, we propose to use deep convolutional networks for automated segmentation of the media-adventitia boundary...
Cell detection in microscopy images is a common and challenging task. We propose a new approach for mitotic cell detection in histopathology images, which is based on a Deep Residual Network architecture combined with Hough voting. We propose a voting layer for neural networks and introduce a novel loss function. Our approach is learned from scratch using cell centroids and the original images. We...
Quantitative imaging biomarkers identification has become a powerful tool for predictive diagnosis given increasingly available clinical imaging data. In parallel, molecular profiles have been well documented in non-small cell lung cancers (NSCLCs). However, there has been limited studies on leveraging the two major sources for improving lung cancer computer-aided diagnosis. In this paper, we investigate...
Detection of Architectural distortion (AD) is important for ruling out possible pre-malignant lesions in breast, but due to its subtlety, it is often missed on the screening mammograms. In this work we suggest a novel AD detection method based on region proposal convolution neural nets (R-CNN). When the data is scarce, as typically the case in medical domain, R-CNN yields poor results. In this study,...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.