Hepatocellular carcinoma (HCC), combined with hepatic hydatid disease, is a rare clinical case, having certain specificity in clinical diagnosis and treatment. We report a case of HCC combined with hepatic hydatid disease treated in our clinic to arouse the attention of clinicians to the disease. A 54-year-old female patient was admitted to the clinic on October 31, 2016 because of “Intermittent upper abdominal pain and discomfort for 1 month.” Abdominal CT in the previous hospital showed liver space-occupying lesions, and hepatic hydatid disease should be considered. The patient had a history of hepatitis B virus (HBV) infection since childhood but has not received antiviral treatment. She did have a history of life in pastoral areas. Laboratory examination results were as follows: alpha-fetoprotein (AFP) 1,210 ng/ml, HBV DNA: 5.32E + 3 IU/ml. Casoni test was positive. Enhanced CT of abdomen suggestion was: malignant liver tumor, hepatic hydatid disease. Gastroscopy and colonoscopy showed no abnormalities. She underwent an operation on November 10, 2016. Segment 5, 8 of hepatic, echinococcus internal capsule, and cholecyst were all removed. She took albendazole (0.4 g/day) for 6 months and oral entecavir (0.5 mg/day) antiviral treatment for a long time after surgery. From May 2017 to October 2019, a total of 5 cycles of transarterial chemotherapy embolization (TACE) were performed. The patient underwent surgical treatment, followed by TACE, antiviral therapy, and sequential albendazole treatment. The AFP level increased significantly, but there was no obvious recurrence of HCC in imaging.

Gastric cancer is the fifth most common cancer worldwide, and the treatment of advanced gastric cancer has relatively little progress. With the continuous development of molecularly targeted therapy for tumors, it has been discovered that human epidermal growth factor receptor 2 (HER2) contributes to the poor prognosis and pathogenesis of various cancers. In order to treat HER2-positive advanced gastric cancer, Trastuzumab has emerged as the first first-line targeted medication used in conjunction with chemotherapy. The consequent trastuzumab resistance has become an important issue, and various new HER2-targeted gastric cancer drugs are emerging to address this challenge. This review’s primary concern is the drug mechanism of various HER2-positive gastric cancer targeted therapy and fresh techniques of detection.

3D reconstruction technology is one of the important technologies of computer vision. Compared with 2D data, 3D space contains more abundant information, including location information, local / global features and so on. It is of great significance to solve the problem of target distance measurement in a single image scene. It is difficult for neural network to predict hole size without image scale information. In this paper, we focus on the measurement of objects with little change in height in the image. We use the relationship between camera parameters and regional features and the internal relationship between regional features to solve the problems of three-dimensional parameter reconstruction and monocular image distance measurement and use the standard cross entropy loss to optimize the transformer model. This model has achieved good results on the sampled data set.

In the actual construction process, the supervision work of concrete pouring has many problems, such as heavy workload, low efficiency, misjudgment and omission. Deep learning shows good performance in computer vision, such as semantic segmentation and object recognition. In this paper, semantic segmentation is used to identify the position of vibrating bar in concrete pouring to provide a basis for detecting whether the vibrating behavior is standardized. Existing semantic segmentation studies ignore whether the edges of objects are finely detected. Recently, contrastive learning has made progress in computer vision. In addition, the training data differs greatly from the actual construction scene, namely domain shift. Therefore, we proposed the domain-adaptation method BECA which consists of two parts: boundary enhancement for accurate detection of edges and contrastive alignment for domain shift. Experiments show that the proposed BECA has unique advantages compared with the previous methods.