KEYWORDS: Networks, X-rays, Anatomy, Education and training, Diseases and disorders, Optical engineering, Medical imaging, Lithium, Integrated circuits, Head
Dental oral disease is one of the most prevalent diseases worldwide, as of a medical analysis in The Lancet 2022[1]. The most common oral diseases worldwide are dental caries (cavities), periodontal disease, tooth loss, and overdevelopment of the jaw caused by excessive unilateral chewing. Dental radiography plays a very important role in clinical diagnosis, treatment and surgery. Automatic segmentation of medical lesions is a prerequisite for efficient clinical analysis. Therefore, accurate positioning of anatomical landmarks is a crucial technique for clinical diagnosis and treatment planning. In this paper, we propose a novel deep network to detect anatomical landmarks. Our proposed network consists of a multi-scale feature aggregation module for channel attention and a deep network for feature refinement. To demonstrate the superiority of our network, training comparisons with several popular networks are performed on the same dataset. The end result is that our network outperforms several popular networks today in both mean radial error (MRE) and successful detection rate (SDR).
Compared with the classical object detection algorithms with horizontal bounding box, such as Yolo and Faster-R-CNN, the oriented object detector in remote sensing images can be more robust. Considering that the challenge of oriented object detection in remote sensing images, we propose a cross-layer feature fusion improvement strategy. Specifically, to obtain multiple layer feature maps for subsequent object detection, the shallow feature map with texture information merged with the deep feature map with semantic features. The spatial attention mechanism is introduced to enhance our algorithm's attention to the non-local information in these feature maps. Extensive experiments on a public dataset, DOTA, demonstrate the effectiveness of our proposed method. Under the large difference of object scale, arbitrary orientation of object, objects with dense arrangement and complex background, the experimental results show that our method has better performance.
Affective computing is an interdisciplinary research area that includes machine learning and pattern recognition, psychology, and cognitive science. The aim is to research and develop theories, methods and systems that can recognize, interpret, process and simulate human emotions. In this article we propose a neural network model for multimodal emotion recognition based on cross-media data-feature fusion. Multimodal data fusion can effectively improve the accuracy of emotion recognition. We extract features from EEG data and facial images using a deep double-stream neural network and then merge them in a medium-term feature layer to identify three categories of emotions (sadness, calm, and happiness). The experimental results show that the detection accuracy can reach over 95%. Compared to the traditional single-modal emotion recognition method, the accuracy rate of emotion recognition based on EEG data and facial images has been significantly improved. It also proves that the multimodal medium-term feature layer fusion method has good applicability for emotion recognition.
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