Ship visible images detection based on computer vision plays an important role in the field of intelligent ship. To increase the model speed, accuracy, and reduce the parameters of the model to facilitate the deployment on hardware devices in practical applications, this study proposed a new model named STD-Yolov5. Firstly, the attention mechanism module of ECA was embedded in backbone to enhance the feature extraction capability of the network. Secondly, GAFPN was designed to reduce the parameters and GFLOPs. Thirdly, to solve the problem of ship-type false detection and missing detection, this paper presented a new receptive field amplification module named GSPP. Finally, replaced the GIoU bounding box regression loss function with a simpler generalisation of alpha-GIoU to improve the accuracy of the model. Compared to Yolov5, the mAP@.5:.95 of STD-Yolov5 increased by 1.2%, the parameters decreased by 24.85%, and the GFLOPs decreased by 14.46%.

Bronchiectasis is defined as a permanent dilation of the bronchi that can cause pulmonary ventilation dysfunction. CT examination is an important means of diagnosing bronchiectasis. It can also be used in severity scoring. Current studies on bronchiectasis have focused on high-resolution CT (HRCT), ignoring the more common low-dose CT (LDCT). Methodologically, existing studies have not adopted an authoritative standard to classify the severity of bronchiectasis. In effect, the accuracy of detection and classification needs to be improved for practical application. In this paper, the ACER image enhancement method, RDU-Net lung lobe segmentation method and HDC Mask R-CNN model were proposed to detect and classify bronchiectasis. Moreover, a Python-based system was developed: after inputing an LDCT image of a patient’s lung, it can automatically perform a series of processing, then call on the trained deep learning model for detection and classification, and automatically obtain the patient’s bronchiectasis final score according to the Reiff and BRICS scoring criteria. In this paper, the mapping relationship between original lung CT image data and bronchiectasis scoring system was established. The accuracy of the method proposed in this paper was 91.4%; the IOU, sensitivity and specificity were 88.8%, 88.6% and 85.4%, respectively; and the recognition speed of one picture was about 1 s. Compared to a human doctor, the system can process large amounts of data simultaneously, quickly and efficiently, with the same judgment accuracy as a human doctor. Doctors only need to judge the uncertain cases, which significantly reduces the burden of doctors and provides a useful reference for doctors to diagnose the disease.

Bronchiectasis can cause pulmonary ventilation dysfunction, which will bring huge social and economic burden. Deep learning methods are rarely used in the detection and classification of bronchiectasis. Current studies on bronchiectasis mainly focus on high resolution CT (HRCT), ignoring the more common low-dose CT (LDCT). Methodologically, existing studies do not use an authoritative standard to classify the severity of bronchiectasis. In effect, the accuracy of detection and classification needs to be improved for practical application. According to the above problems, we adopt LDCT data, contrast two deep learning models for the detection and classification of bronchiectasis effect, then we use dilated convolution to promote deep learning model for detection and classification of bronchiectasis. Finally, we developed an automatic detection and scoring system for bronchiectasis combining with authoritative scoring standards. According to the experiments that the detection rate of bronchiectasis in LDCT images by our developed bronchiectasis detection and scoring system can reach 91.0

支气管扩张症是一种常见的慢性呼吸道疾病,严重影响患者的生活质量,带来了沉重的社会经济负担.随着人工智能的发展,可利用计算机视觉领域的目标检测技术辅助诊断这类疾病.报告了支气管扩张症人工智能诊断系统的研究现状,介绍了支气管扩张症的临床诊断方式,并基于此提出了计算机辅助诊断该类疾病的诊断技术路线,总结了CT影像噪声抑制、肺实质提取、肺叶分割的传统和深度学习方法,针对支气管扩张金标准数据集匮乏的问题,从两个方面综述了目标检测应用于计算机辅助诊断的问题及挑战,详细比较了不同算法的特点和适用场景.最后讨论了未来可能的发展趋势.

The application of ship detection for assistant intelligent ship navigation has stringent requirements for the model’s detection speed and accuracy. In response to this problem, this study uses an improved YOLO-V4 detection model (ShipYOLO) to detect ships. Compared to YOLO-V4, the model has three main improvements. Firstly, the backbone network (CSPDarknet) of YOLO-V4 is optimized. In the training process, the 3 × 3 convolution, 1 × 1 convolution, and identity parallel mode are used to replace the original feature extraction component (ResUnit) and more features are extracted. In the inference process, the branch parameters are combined to form a new backbone network named RCSPDarknet, which improves the inference speed of the model while improving the accuracy. Secondly, in order to solve the problem of missed detection of the small-scale ships, we designed a new amplified receptive field module named DSPP with dilated convolution and Max-Pooling, which improves the model’s acquisition of small-scale ship spatial information and robustness of ship target space displacement. Finally, we use the attention mechanism and Resnet’s shortcut idea to improve the feature pyramid structure (PAFPN) of YOLO-V4 and get a new feature pyramid structure named AtFPN. The structure effectively improves the model’s feature extraction effect for ships of different scales and reduces the number of model parameters, further improving the model’s inference speed and detection accuracy. In addition, we have created a ship dataset with a total of 2238 images, which is a single-category dataset. The experimental results show that ShipYOLO has the advantage of faster speed and higher accuracy even in different input sizes. Considering the input size of 320 × 320 on the PC equipped with NVIDIA 1080Ti GPU, the FPS and mAP@5 : 5:95 (mAP90) of ShipYOLO are increased by 23.7% and 13.6% (10.6%), respectively, with an input size of 320 × 320, ShipYOLO, compared to YOLO-V4.

目的 探讨“Sandwich”教学法在医学影像学教学中的应用价值.方法 选取2018年度、2019年度山东大学口腔医学专业五年制学生分别作为对照组和实验组,对照组为传统教学组,实验组为“Sandwich”教学组.然后统一标准考核,比较考核成绩.结果 “Sandwich”教学组成绩优于传统教学组(P＜0.05).结论 “Sandwich”教学法使学生与老师、学生与学生之间增强交流,能充分调动学生的学习积极性,提高学生的自主学习、表达沟通和临床思维能力.

支气管扩张是一种常见疾病，可引起肺通气功能障碍且病程长，会造成巨大的社会经济负担。使用深度学习的方法对支气管扩张进行检测与分类，使其在判断准确率与人类医生相当的情况下，快速高效地对大量数据同时进行处理，不仅能够减轻医生负担，还能为医生诊断支气管扩张提供有用的参考，具有很高的价值和意义。图像预处理的好坏是影响深度学习效果的一个重要因素。肺叶分割则是处理肺部CT图像的一个难点与重点。本研究使用基于U-Net的肺叶分割方法处理低剂量CT(LDCT)数据，然后使用深度学习模型Mask R-CNN对支气管扩张进行检测与分类。实验表明，使用肺叶分割的预处理方法可以有效提升深度学习模型对支气管扩张的检测与分类效果。本实验最后训练好的模型对使用了肺叶分割后的LDCT图像中支气管扩张的检测准确率可以达到89.8%，平均分类准确率为91.0%，识别一张图片的速度约为1.5s。