The application of lightweight AI algorithms in postoperative rehabilitation of breast cancer.

The prevalence of breast cancer as a major global cancer has underscored the importance of postoperative recovery for breast cancer patients. Among the issues, postoperative patients are prone to spinal deformities, including scoliosis, which has drawn significant attention from healthcare professionals. The primary aim of this study is to design a postoperative recovery platform for breast cancer patients that can effectively detect posture changes, provide feedback and support to medical staff, assist doctors in formulating recovery plans, and prevent spinal deformities. The feasibility of the recovery platform is also validated through experiments. The development and validation of the experimental recovery platform. The recovery platform includes instrument design, patient data collection, model training and fine-tuning, and postoperative body posture evaluation by comparing preoperative and postoperative conditions. The evaluation results are provided to doctors to facilitate the formulation of personalized postoperative recovery plans. This paper comprehensively designs and implements the recovery platform and verifies its feasibility through simulation experiments. Statistical methods were employed for the validation of the rehabilitation platform in simulated experiments, with a significance level of p < 0.05. In comparison to static assessments like CT scans, this paper introduces a dynamic detection method that provides a more insightful analysis of body posture. The experiments also demonstrate the preventive capability of this method against post-operative spinal deformities, ultimately enhancing patients' self-image, restoring their confidence, and enabling them to lead more fulfilling lives.

Patch Matching and Dense CRF-Based Co-Refinement for Building Change Detection from Bi-Temporal Aerial Images.

The identification and monitoring of buildings from remotely sensed imagery are of considerable value for urbanization monitoring. Two outstanding issues in the detection of changes in buildings with composite structures and relief displacements are heterogeneous appearances and positional inconsistencies. In this paper, a novel patch-based matching approach is developed using densely connected conditional random field (CRF) optimization to detect building changes from bi-temporal aerial images. First, the bi-temporal aerial images are combined to obtain change information using an object-oriented technique, and then semantic segmentation based on a deep convolutional neural network is used to extract building areas. With the change information and extracted buildings, a graph-cuts-based segmentation algorithm is applied to generate the bi-temporal changed building proposals. Next, in the bi-temporal changed building proposals, corner and edge information are integrated for feature detection through a phase congruency (PC) model, and the structural feature descriptor, called the histogram of orientated PC, is used to perform patch-based roof matching. We determined the final change in buildings by gathering matched roof and bi-temporal changed building proposals using co-refinement based on CRF, which were further classified as "newly built," "demolished", or "changed". Experiments were conducted with two typical datasets covering complex urban scenes with diverse building types. The results confirm the effectiveness and generality of the proposed algorithm, with more than 85% and 90% in overall accuracy and completeness, respectively.

Building Change Detection from Bi-Temporal Dense-Matching Point Clouds and Aerial Images.

In this work, a novel building change detection method from bi-temporal dense-matching point clouds and aerial images is proposed to address two major problems, namely, the robust acquisition of the changed objects above ground and the automatic classification of changed objects into buildings or non-buildings. For the acquisition of changed objects above ground, the change detection problem is converted into a binary classification, in which the changed area above ground is regarded as the foreground and the other area as the background. For the gridded points of each period, the graph cuts algorithm is adopted to classify the points into foreground and background, followed by the region-growing algorithm to form candidate changed building objects. A novel structural feature that was extracted from aerial images is constructed to classify the candidate changed building objects into buildings and non-buildings. The changed building objects are further classified as "newly built", "taller", "demolished", and "lower" by combining the classification and the digital surface models of two periods. Finally, three typical areas from a large dataset are used to validate the proposed method. Numerous experiments demonstrate the effectiveness of the proposed algorithm.