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Aim of the databaseThe Danish Head and Neck Cancer database is a nationwide clinical quality database that contains prospective data collected since the early 1960s. The overall aim of this study was to describe the outcome of the national strategy for multidisciplinary treatment of head and neck cancer in Denmark and to create a basis for clinical trials.Study populationThe study population consisted of all Danish patients referred for treatment of squamous cell carcinoma of the larynx, pharynx, oral cavity, or neck nodes from unknown primary or any histopathological type (except lymphoma) of cancer in the nasal sinuses, salivary glands, or thyroid gland (corresponding to the International Classification of Diseases, tenth revision, classifications C.01–C.11, C.30–C.32, C.73, and C.80).Main variablesThe main variables used in the study were symptoms and the duration of the symptoms; etiological factors; pretreatment and diagnostic evaluation, including tumor–node–metastasis classification, imaging, histopathology, and laboratory tests; primary treatment with semidetailed information of radiotherapy, surgery, and medical treatment; follow-up registration of tumor status and side effects; registration of relapse and treatment thereof; and registration of death and cause of death.Main resultsData from >33,000 patients have been recorded during a period of >45 years. In this period, the outcome of treatment improved substantially, partly due to better treatment as a result of a series of continuous clinical trials and subsequent implementation in national guidelines. The database has furthermore been used to describe the effect of reduced waiting time, changed epidemiology, and influence of comorbidity and socioeconomic parameters.ConclusionHalf a century of registration of head and neck cancer treatment and outcome has created the basis for understanding and has substantially contributed to improve the treatment of head and neck cancer at both national and international levels.

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Speed detection of a moving object using an optical camera has always been an important subject to study in computer vision. This is one of the key components to address in many application areas, such as transportation systems, military and naval applications, and robotics. In this study, we implemented a speed detection system for multiple moving objects on the ground from a moving platform in the air. A detect-and-track approach is used for primary tracking of the objects. Faster R-CNN (region-based convolutional neural network) is applied to detect the objects, and a discriminative correlation filter with CSRT (channel and spatial reliability tracking) is used for tracking. Feature-based image alignment (FBIA) is done for each frame to get the proper object location. In addition, SSIM (structural similarity index measurement) is performed to check how similar the current frame is with respect to the object detection frame. This measurement is necessary because the platform is moving, and new objects may be captured in a new frame. We achieved a speed accuracy of 96.80% with our framework with respect to the real speed of the objects.

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