Semi-Supervised Segmentation Framework for Gastrointestinal Lesion Diagnosis in Endoscopic Images.

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.

Annotating Early Esophageal Cancers Based on Two Saliency Levels of Gastroscopic Images.

Early 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.

Identification of lesion images from gastrointestinal endoscope based on feature extraction of combinational methods with and without learning process.

The 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.

Essentiality drives the orientation bias of bacterial genes in a continuous manner.

Studies 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.