Auto-segmentation quality or accuracy influences their clinical usefulness. However, currently widely utilized segmentation metrics (e.g., Dice Coefficient (DC) and Hausdorff Distance (HD)) cannot effectively express the manual mending effort required when utilizing auto-segmentation results in clinical practice. In this article, we explore ways of evaluating auto-segmentations with clinical efficiency considerations in mind. The time required for correcting auto-segmentations by experts is recorded to indicate ground-truth mending effort. Extended from our previous work, five explicitly-defined metrics are studied in detail for their ability to predict mending effort. More importantly, we explore the use of deep learning networks to provide an implicit metric, which predict mending effort using auto-segmentation masks and original images as input. A 3-institution evaluation is conducted with 7 different anatomic organs in the setting of auto-contouring for radiation therapy planning. Among the five explicit metrics, one form of the proposed Mendability Index (MIhd) shows the best performance to indicate the mending effort for sparse objects with 6.2-14.4% error, while one form of HD (sHD) performs best when assessing large non-sparse objects. Interestingly, while the explicit metrics all require ground truth segmentations for estimating mending effort, the implicit models obtained via deep learning are effective in predicting mending efforts (with 2.9-12.9% error) without the need for ground-truth segmentations and directly from the given image plus the auto-segmentations. We conclude that once effort-predicting deep models are created, it is feasible to assess the clinical usability of new segmentation models, going beyond bench technical evaluation commonly done via explicit metrics.

In order to alleviate urban congestion, improve vehicle mobility, and improve logistics delivery efficiency, this paper establishes a practical multi-objective and multi constraint logistics delivery mathematical model based on graphs, and proposes a solution algorithm framework that combines decomposition strategy and deep reinforcement learning (DRL). Firstly, taking into account the actual multiple constraints such as customer distribution, vehicle load constraints, and time windows in urban logistics distribution regions, a multi constraint and multi-objective urban logistics distribution mathematical model was established with the goal of minimizing the total length, cost, and maximum makespan of urban logistics distribution paths. Secondly, based on the decomposition strategy, a DRL framework for optimizing urban logistics delivery paths based on Graph Capsule Network (G-Caps Net) was designed. This framework takes the node information of VRP as input in the form of a 2D graph, modifies the graph attention capsule network by considering multi-layer features, edge information, and residual connections between layers in the graph structure, and replaces probability calculation with the module length of the capsule vector as output. Then, the baseline REINFORCE algorithm with rollout is used for network training, and a 2-opt local search strategy and sampling search strategy are used to improve the quality of the solution. Finally, the performance of the proposed method was evaluated on standard examples of problems of different scales. The experimental results showed that the constructed model and solution framework can improve logistics delivery efficiency. This method achieved the best comprehensive performance, surpassing the most advanced distress methods, and has great potential in practical engineering.

In this paper, the mathematical model of Vehicle Routing Problem with Time Windows (VRPTW) is established based on the directed graph, and a 3-stage multi-modal multi-objective differential evolution algorithm (3S-MMDEA) is proposed. In the first stage, in order to expand the range of individuals to be selected, a generalized opposition-based learning (GOBL) strategy is used to generate a reverse population. In the second stage, a search strategy of reachable distribution area is proposed, which divides the population with the selected individual as the center point to improve the convergence of the solution set. In the third stage, an improved individual variation strategy is proposed to legalize the mutant individuals, so that the individual after variation still falls within the range of the population, further improving the diversity of individuals to ensure the diversity of the solution set. Based on the synergy of the above three stages of strategies, the diversity of individuals is ensured, so as to improve the diversity of solution sets, and multiple equivalent optimal paths are obtained to meet the planning needs of different decision-makers. Finally, the performance of the proposed method is evaluated on the standard benchmark datasets of the problem. The experimental results show that the proposed 3S-MMDEA can improve the efficiency of logistics distribution and obtain multiple equivalent optimal paths. The method achieves good performance, superior to the most advanced VRPTW solution methods, and has great potential in practical projects.

The dual problem of image super‐resolution (SR), which is referred to as compact‐resolution (CR), and the corresponding image reconstruction are studied. These two problems have been studied independently by the researchers. In this study, a novel model for image CR and the corresponding reconstruction using the reversible network has been proposed. The reversible network has two properties, the first property, lossless information forwarding, which makes the compact‐resolved image retain more information from the original HR image. The second property, bidirectional mapping, by which the forward and reverse propagation of a reversible network can be utilised to implement image CR and reconstruction, respectively, i.e. using the reverse process of image CR to guide the reconstruction. In addition, the utilisation of a reversible network may reduce the size of the model. The superiority of the proposed model was demonstrated by comparing its performance with the state‐of‐the‐art methods on four well‐known benchmark datasets.

The perception of video is different from that of image because of the motion information in video. Motion objects lead to the difference between two neighboring frames which is usually focused on. By far, most papers have contributed to image saliency but seldom to video saliency. Based on scene understanding, a new video saliency detection model with multi-features is proposed in this paper. First, background is extracted based on binary tree searching, then main features in the foreground is analyzed using a multi-scale perception model. The perception model integrates faces as a high level feature, as a supplement to other low-level features such as color, intensity and orientation. Motion saliency map is calculated using the statistic of the motion vector field. Finally, multi-feature conspicuities are merged with different weights. Compared with the gaze map from subjective experiments, the output of the multi-feature based video saliency detection model is close to gaze map.