Accurate recognition of polyps is crucial for early colorectal cancer diagnosis and treatment. Wireless capsule endoscopy (WCE) is a noninvasive, wireless imaging tool that allows direct visualization of the entire colon without discomfort to patients and has the potential to revolutionize the screening workup for colorectal diseases. However, current manual review is laborious and time consuming, requiring the undivided concentration of the gastroenterologist. Computational methods that can assist automated polyp recognition will enhance the outcome both in terms of diagnostic accuracy and efficiency of WCE. This review introduces the computer-assisted algorithms as applied to colorectal polyp screening, focusing on the successes of deep-learning-based strategies in the WCE sequences. We survey key applications of WCE polyp recognition, covering deep-learning-based image-level classification, lesion region detection, and pixel-accurate segmentation. We conclude by discussing emerging research challenges, possible trends, and future directions.

Polyp recognition in colonoscopy images is crucial for early colorectal cancer detection and treatment. However, the current manual review requires undivided concentration of the gastroenterologist and is prone to diagnostic errors. In this article, we present an effective, two-stage approach called

Fruit detection is a fundamental task for automatic yield estimation. The goal is to detect all the fruits in images. The-state of the art of fruit detection algorithm, Faster R-CNN, shows a lack of detection advantage on small fruits. One of the reasons is only that single-level features and a classifier are used for localization of proposal candidates. In this article, we propose to incorporate a multiple classifier fusion strategy into a Faster R-CNN network for small fruit detection. We utilize features from three different levels to learn three classifiers for objectness classification in the stage of proposal localization. Probabilities from classifiers are combined by a simple convolutional layer to generate final objectness classification for proposal candidates. During training, in order to train a model with strong generalization capability, we propose to use correlation coefficients to measure the diversity of multiple classifiers. A novel loss function with classifier correlation is introduced to train the region proposal network. We evaluate the proposed model on two data sets of small fruits. Extensive experiments show that the proposed model outperforms the state-of-the-art detectors for fruit detection.

Robot path planning in 3D space is a challenging problem for its complex configuration. Sampling-based algorithms have gained great success in solving path planning problems in 3D space, but the quality of the initial path is not guaranteed and the convergence to the optimal solution is slow. To address these problems, in this article, we present a novel sampling-based path planning framework enhanced by the deep neural network (DNN) with applications to 3D space. In the proposed framework, we first train the DNN with a number of successful path planning cases in 3D space. Then the DNN is utilized to predict the promising region where the feasible path probably exists for a given path planning problem. This predicted promising region serves as a nonuniform sampling heuristic to bias the sampling process of the path planner. In this way, the path planner can focus on the promising region in the exploration and exploitation process so that the path planning speed gets accelerated. We conduct numerical simulations to evaluate the performance of the proposed algorithm and the results show that it can perform much better than conventional path planning algorithms. Furthermore, we also investigate the performance of different DNN architectures for path planning in 3D space. Note to Practitioners—In this work, we aim to provide an efficient learning-based method to accelerate the robot path planning process in 3D space. Conventional path planning algorithms need to perceive the environment first, and then implement a series of calculations such as collision checking and data storing to generate a feasible path. When facing complex and high-dimensional environments, they do not perform well. But the proposed neural network method in this article can predict the promising region where the feasible path exists for any given environment. This prediction result is used to guide the path planning process so that the algorithm performance can get significantly improved. Apart from sampling-based algorithms, the proposed neural network model can also be extended to other types of path planning algorithms.