Graph based construction of textured large field of view mosaics for bladder cancer diagnosis

Scalable gastroscopic video summarization via similar-inhibition dictionary selection

“…Many computer-aided endoscopy diagnosis systems have been proposed to assist clinicians in improving the accuracy of medical diagnosis using the images or videos recorded in the inspection of a GT tract. According to the specific lesions, these systems can be classified to handle bleeding [30,31], tumors [32,33], Helicobacter pylori [34], cancer [35,36], Crohn's disease [37] and polyps [38]. Moreover, some other applications include pose detection for endoscopy [39], video segmentation [40] and three-dimensional reconstruction of the digestive wall [41].…”

Deep sparse feature selection for computer aided endoscopy diagnosis

“…Depending on the areas in gastrointestinal tract (GI), the methods can be broken down for the esophagus [16], the stomach [17,7], the small intestine [2][3][4][5] and the colon [9,18]. Depending on the specific lesions, the diagnosis methods can be classified to handle bleeding [2], cancer [19,17], Celiac disease, Helicobacter pylori [7], polyps [20,14] and ulcers [4], motility assessment [21], tumors [6,7], Barrett's esophagus, Crohn's disease [9,18], and just classify the region into normal and abnormal [22]. Some other applications include detecting informative frames [3], WCE color video segmentation [23], summarization [24] and clustering [25].…”

Flexible calibration of structured-light systems projecting point patterns

“…This texture is quite realistic since even urologists cannot distinguish pig bladder textures from human bladder textures. proposed in 2D for the bladder [23,[38][39][40]. In the past, different authors have shown that 3D information can be recovered with active vision endoscopes.…”