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One of the focal points in Geographic Information Science (GIS) is to uncover the spatial distribution patterns of geographical phenomena. In response to the insufficient spatial feature learning concerning neighborhoods in traditional machine learning-based Cellular Automata (CA) models for land use change, this study couples the Random Forest (RF) model and the Spatially Non-Stationary Convolutional Neural Network (SNSCNN) model to the CA model. The resulting RF-SNSCNN-CA model considers the issue of spatial non-stationarity by incorporating attention mechanisms. Using observed urban land change data from 2010 to 2017 in the 21 districts of Chongqing’s main city as an example, two sets of experiments comprising eight scenarios were designed to verify the neighborhood effects. The results demonstrate that the proposed RF-SNSCNN-CA model achieves an Overall Accuracy (OA) of 97.82%, Kappa of 0.7683, and Figure of Merit (FoM) of 0.3836. The study reveals the following findings. Firstly, the RF-SNSCNN-CA model integrates the dual advantages of traditional machine learning and deep learning models, in which SNSCNN improves by the combined effect of channel and spatial attention mechanisms improves the learning of neighborhood features; secondly, the machine learning-like urban sprawl CA modeling process, regardless of the approach taken to obtain development suitability, cannot completely replace the learning of the neighborhood part; lastly, the use of traditional neighborhood modeling methods may produce suppression of simulation results and make the model inadequately learn spatial features.

In this paper, a co-segmentation algorithm based on 3D heat diffusion named co-diffusion is proposed. The image set is considered as a metal cuboid, and the K heat sources with constant temperature, which maximize the sum of the temperature of the system under anisotropic heat diffusion, are found to cluster the image set. The co-diffusion co-segmentation is an intuitive extension of the diffusion segmentation in Kim et al. (Proceedings of ICCV, 2011) while the performance is greatly improved. Comparatively, the proposed algorithm advances in the following three aspects: (1) The proposed algorithm can obtain better optimization because the heat diffusion is directly solved in 3D image set space, while the algorithm in Kim et al. (2011) deals with many independent 2D heat diffusions and solves the optimization by approximate belief propagation. (2) The marginal gain of every candidate heat source is globally determined in the image set, which can effectively compensate the wrong segmentations caused by the locality of the 2D image diffusion (Kim et al. 2011). (3) The K heat sources are chosen in image set while the algorithm (Kim et al. 2011) appoints mandatory K heat sources to each image in set, which will inevitably cause wrong segmentations for some images. The superiority of the proposed co-diffusion segmentation method is examined and demonstrated through a large number of experiments by using some typical datasets.

In this paper, an object segmentation algorithm based on automatic shape constraint selection is proposed. Different from the traditional shape prior based object segmentation methods which only provide loose shape constraints, our proposed object segmentation gives more accurate shape constraint by selecting the most appropriate shape among the standard shape set. Furthermore, to overcome the inevitable differences between the true borders and the standard shapes, the Coherent Point Drift (CPD) is adopted to project the standard shapes to the local ones. A quantitative evaluating mechanism is introduced to pick out the most suitable shape prior. The proposed algorithm mainly consists of four steps: 1) the initial GrabCut segmentation; 2) standard shape projection by CPD registration; 3) rank the standard shapes according to the evaluation scores; 4) refine GrabCut segmentation with the chosen shape constraint. The comparison experiments with the related algorithms on Weizmann_horse dataset have demonstrated the good performance of the proposed algorithm.

Locally advanced lung cancer includes IIIA and IIIB lung cancer that tumors are localized within the chest and with no clinic and pathologic distal metastasis. In this study the results of extended resection of a portion of heart or great vessels with cardiopulmonary bypass was summarized in the treatment of locally advanced lung cancer.Lobectomy or pneumonectomy combined with extended partial excision of the heart or great vessels were carried out in 10 patients with locally advanced lung cancer. The operations included aortic resection and reconstruction with left heart bypass in 2 cases, extended resection of left atrium with normal cardiopulmonary bypass in 5 cases, and resection and reconstruction of superior vena cava in 3 cases respectively.The patients had no operative complication except for one haemothorax, which was controlled by re-exploration. One patient died of brain metastasis 6 months after operation and another one died of multiple metastasis 26 months after operation. The others were alive.CPB is a safe and effective anesthetic procedure during extended resection of locally advanced lung cancer although it is controversial for aggravating operative trauma, complex technique and higher cost.