Real-time gastric polyp detection using convolutional neural networks

Abstract

Computer-aided polyp detection in gastric gastroscopy has been the subject of research over the past few decades. However, despite significant advances, automatic polyp detection in real time is still an unsolved problem. In this paper, we report on a convolutional neural network (CNN) for polyp detection that is constructed based on Single Shot MultiBox Detector (SSD) architecture and which we call SSD for Gastric Polyps (SSD-GPNet). To take full advantages of feature maps’ information from the feature pyramid and to acquire higher accuracy, we re-use information that is abandoned by Max-Pooling layers. In other words, we reuse the lost data from the pooling layers and concatenate that data as extra feature maps to contribute to classification and detection. Meanwhile, in the feature pyramid, we concatenate feature maps of the lower layers and feature maps that are deconvolved from upper layers to make explicit relationships between layers and to effectively increase the number of channels. The results show that our enhanced SSD for gastric polyp detection can realize real-time polyp detection with 50 frames per second (FPS) and can improve the mean average precision (mAP) from 88.5% to 90.4%, with only a little loss in time-performance. And the further experiment shows that SSD-GPNet has excellent performance in improving polyp detection recalls over 10% (p = 0.00053), especially in small polyp detection. This can help endoscopic physicians more easily find missed polyps and decrease the gastric polyp miss rate. It may be applicable in daily clinical practice to reduce the burden on physicians.

Strategies for Colorectal Cancer Screening

“…111,112 Computer-aided detection and histologic diagnosis systems are available and have shown promising results. [113][114][115][116] However, adequate mucosal exposure and lesion resection will remain operator dependent. The most immediate goal is developing a real-time colonoscopy Regardless of screening strategy, patient participation will always be a key determinant of success.…”

Artificial intelligence in gastrointestinal endoscopy

“…79 Compared with human endoscopists, the CNN system was able to differentiate early gastric cancer from deeper submucosal invasion with a higher accuracy (by 17.25%; 95% CI, 11.63%-22.59%) and specificity (by 32.21%; 95% CI, 26.78-37.44). Other CNN systems have been developed for detection of gastric polyps 80 and chronic atrophic gastritis from images of the proximal stomach 81 and distal stomach. 82 An algorithm to detect gastric and esophageal cancer was developed based on a dataset of 1,036,496 images from 84,424 individuals and validated on both an external retrospective dataset (28,663 cancer and 783,876 control images) and prospective dataset (4317 cancer and 62,433 control images).…”

“… 79 Compared with human endoscopists, the CNN system was able to differentiate early gastric cancer from deeper submucosal invasion with a higher accuracy (by 17.25%; 95% CI, 11.63%-22.59%) and specificity (by 32.21%; 95% CI, 26.78-37.44). Other CNN systems have been developed for detection of gastric polyps 80 and chronic atrophic gastritis from images of the proximal stomach 81 and distal stomach. 82 …”

Convolutional Neural Network Technology in Endoscopic Imaging: Artificial Intelligence for Endoscopy

“…For instance, Zhang et al achieved an 84% F1-score at speeds of 50 frames-per-second with a modified Single Shot MultiBox Detector. 12,53 In addition, a study by Urban et al 51 suggested that a CNN is capable of detecting polyps regardless of their morphological type (polypoid or nonpolypoid): in general, a nonpolypoid polyp is challenging to detect because of its shape. Zheng et al proposed a real-time polyp detector based on You-Only-Look-Once in endoscopic images.…”