Colorectal polyps are critical indicators of colorectal cancer (CRC). Classification of polyps during colonoscopy is still a challenge for which many medical experts have come up with visual models, albeit with limited success. An early detection of CRC prevents further complications in the colon, which makes identification of abnormal tissue a crucial step during routinary colonoscopy. In this paper, a classification approach is proposed to differentiate between benign and pre-malignant polyps using features learned from a Triplet Network architecture. The study includes a total of 154 patients, with 203 different polyps. For each polyp an image is acquired with White Light (WL), and additionally with two recent endoscopic modalities:Blue Laser Imaging (BLI) and Linked Color Imaging (LCI). The network is trained with the associated triplet loss, allowing the learning of non-linear features, which prove to be a highly discriminative embedding, leading to excellent results with simple linear classifiers. Additionally, the acquisition of multiple polyps with WL, BLI and LCI, enables the combination of the posterior probabilities, yielding a more robust classification result. Threefold cross-validation is employed as validation method and accuracy, sensitivity, specificity and area under the curve (AUC) are computed as evaluation metrics. While our approach achieves a similar classification performance compared to state-of-the-art methods, it has a much lower inference time (from hours to seconds, on a single GPU). The increased robustness and much faster execution facilitates future advances towards patient safety and may avoid time-consuming and costly histhological assessment.
Colorectal cancer (CRC) is one of the leading causes of cancer-related deaths. Since most CRCs develop from colorectal polyps (CRPs), accurate endoscopic differentiation facilitates decision making on resection of CRPs, thereby increasing cost-efficiency and reducing patient risk. Current classification systems based on whitelight imaging (WLI) or narrow-band imaging (NBI) have limited predictive power, or they do not consider sessile serrated adenomas/polyps (SSA/Ps), although these cause up to 30% of all CRCs. To better differentiate adenomas, hyperplastic polyps, and SSA/Ps, this paper explores the feasibility of two approaches: (1) an accurate computer-aided diagnosis (CADx) system for automated diagnosis of CRPs, and (2) novel endoscopic imaging techniques like blue-light imaging (BLI) and linked-color imaging (LCI). Two methods are explored to predict histology: (1) direct classification using a support vector machine (SVM) classifier, and (2) classification via a clinical classification model (WASP classification) combined with an SVM. The use of probabilistic features of SVM facilitates objective quantification of the detailed classification process. Automated differentiation of colonic polyp subtypes reaches accuracies of 78−96%, thereby improving medical expert results by 4−20%. Diagnostic accuracy for directly predicting adenomatous from hyperplastic histology reaches 93% and 87−90% using NBI and the novel BLI and LCI techniques, respectively, thus improving medical expert results by 26% and 20−23%, respectively. Predicting adenomatous histology in diminutive polyps with high confidence yields NPVs of 100%, clearly satisfying the PIVI guideline recommendation on endoscopic innovations (≥90% NPV). Our CADx system outperforms clinicians, while the novel BLI technique adds performance value.