Colorectal cancer has a great incidence rate worldwide, but its early detection significantly increases the survival rate. Colonoscopy is the gold standard procedure for diagnosis and removal of colorectal lesions with potential to evolve into cancer and computer-aided detection systems can help gastroenterologists to increase the adenoma detection rate, one of the main indicators for colonoscopy quality and predictor for colorectal cancer prevention. The recent success of deep learning approaches in computer vision has also reached this field and has boosted the number of proposed methods for polyp detection, localization and segmentation. Through a systematic search, 35 works have been retrieved. The current systematic review provides an analysis of these methods, stating advantages and disadvantages for the different categories used; comments seven publicly available datasets of colonoscopy images; analyses the metrics used for reporting and identifies future challenges and recommendations. Convolutional neural networks are the most used architecture together with an important presence of data augmentation strategies, mainly based on image transformations and the use of patches. End-to-end methods are preferred over hybrid methods, with a rising tendency. As for detection and localization tasks, the most used metric for reporting is the recall, while Intersection over Union is highly used in segmentation. One of the major concerns is the difficulty for a fair comparison and reproducibility of methods. Even despite the organization of challenges, there is still a need for a common validation framework based on a large, annotated and publicly available database, which also includes the most convenient metrics to report results. Finally, it is also important to highlight that efforts should be focused in the future on proving the clinical value of the deep learning based methods, by increasing the adenoma detection rate.

结直肠癌在世界范围内的发病率很高，但其早期发现显着提高了生存率。结肠镜检查是诊断和切除有可能演变成癌症的结直肠病变的金标准程序，计算机辅助检测系统可以帮助胃肠病学家提高腺瘤检出率，这是结肠镜检查质量的主要指标之一和结直肠癌预防的预测指标。最近计算机视觉中深度学习方法的成功也达到了这一领域，并增加了用于息肉检测、定位和分割的提议方法的数量。通过系统检索，检索到35件作品。当前的系统评价对这些方法进行了分析，说明了所使用的不同类别的优缺点；评论七个公开可用的结肠镜图像数据集；分析用于报告的指标并确定未来的挑战和建议。卷积神经网络是最常用的架构，同时也是数据增强策略的重要存在，主要基于图像转换和补丁的使用。端到端方法优于混合方法，并有上升趋势。对于检测和定位任务，报告中最常用的指标是召回率，而联合上的交集在分割中使用率很高。主要问题之一是难以对方法进行公平比较和重现。即使组织挑战，仍然需要一个基于大型、带注释和公开可用的数据库的通用验证框架，其中还包括报告结果的最方便的指标。最后，还需要强调的是，未来应该通过提高腺瘤检出率来证明基于深度学习的方法的临床价值。