Background: Accurate gastrointestinal (GI) lesion segmentation is crucial in diagnosing digestive tract diseases. An automatic lesion segmentation in endoscopic images is vital to relieving physicians' burden and improving the survival rate of patients. However, pixel-wise annotations are highly intensive, especially in clinical settings, while numerous unlabeled image datasets could be available, although the significant results of deep learning approaches in several tasks heavily depend on large labeled datasets.
View Article and Find Full Text PDFEarly diagnoses of esophageal cancer can greatly improve the survival rate of patients. At present, the lesion annotation of early esophageal cancers (EEC) in gastroscopic images is generally performed by medical personnel in a clinic. To reduce the effect of subjectivity and fatigue in manual annotation, computer-aided annotation is required.
View Article and Find Full Text PDFThe gastrointestinal endoscopy in this study refers to conventional gastroscopy and wireless capsule endoscopy (WCE). Both of these techniques produce a large number of images in each diagnosis. The lesion detection done by hand from the images above is time consuming and inaccurate.
View Article and Find Full Text PDFStudies had found that bacterial genes are preferentially located on the leading strands. Subsequently, the preferences of essential genes and highly expressed genes were compared by classifying all genes into four groups, which showed that the former has an exclusive influence on orientation. However, only some functional classes of essential genes have this orientation bias.
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