In many computerized methods for cell detection, segmentation, and classification in digital histopathology that have recently emerged, the task of cell segmentation remains a chief problem for image processing in designing computer-aided diagnosis (CAD) systems. In research and diagnostic studies on cancer, pathologists can use CAD systems as second readers to analyze high-resolution histopathological images. Since cell detection and segmentation are critical for cancer grade assessments, cellular and extracellular structures should primarily be extracted from histopathological images. In response, we sought to identify a useful cell segmentation approach with histopathological images that uses not only prominent deep learning algorithms (i.e., convolutional neural networks, stacked autoencoders, and deep belief networks), but also spatial relationships, information of which is critical for achieving better cell segmentation results. To that end, we collected cellular and extracellular samples from histopathological images by windowing in small patches with various sizes. In experiments, the segmentation accuracies of the methods used improved as the window sizes increased due to the addition of local spatial and contextual information. Once we compared the effects of training sample size and influence of window size, results revealed that the deep learning algorithms, especially convolutional neural networks and partly stacked autoencoders, performed better than conventional methods in cell segmentation.
In this study, it is aimed to minimize the labor and time spent during the updating of e-content and re-uploading them into learning management systems, which is one of the present e-learning problems. For this purpose, a new e-book template has been designed. Transferring content into e-book consists of two important steps. Firstly, the contents prepared in MS Word program are transformed into XML. Later, the contents transformed into XML are read by e-book template. EBONI design principles have been highly obeyed during the design of the template. Some of the EBONI design principles used in the e-book template are book cover, contents, short page contents, legible writing format, multimedia and interactive items that may activate users. Thanks to these, an e-learning environment that is visually rich, easy to understand and follow with flexible contents has been attempted to create.
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