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Convolutional neural networks (CNNs) have made significant progress in the field of facial expression recognition (FER). However, due to challenges such as occlusion, lighting variations, and changes in head pose, facial expression recognition in real-world environments remains highly challenging. At the same time, methods solely based on CNN heavily rely on local spatial features, lack global information, and struggle to balance the relationship between computational complexity and recognition accuracy. Consequently, the CNN-based models still fall short in their ability to address FER adequately. To address these issues, we propose a lightweight facial expression recognition method based on a hybrid vision transformer. This method captures multi-scale facial features through an improved attention module, achieving richer feature integration, enhancing the network's perception of key facial expression regions, and improving feature extraction capabilities. Additionally, to further enhance the model's performance, we have designed the patch dropping (PD) module. This module aims to emulate the attention allocation mechanism of the human visual system for local features, guiding the network to focus on the most discriminative features, reducing the influence of irrelevant features, and intuitively lowering computational costs. Extensive experiments demonstrate that our approach significantly outperforms other methods, achieving an accuracy of 86.51% on RAF-DB and nearly 70% on FER2013, with a model size of only 3.64 MB. These results demonstrate that our method provides a new perspective for the field of facial expression recognition. [ABSTRACT FROM AUTHOR]

Wireless capsule endoscopy (WCE) is becoming more popular in clinical settings as a safe and painless gastrointestinal examination. Existing studies on automatic detection of lesions in WCE images have the problems of small dataset size and uneven distribution of numbers in terms of categories, which often leads to overfitting of the model and severely limits the performance improvement of the object detection network on WCE images. The traditional data enhancement methods such as flipping and local erasure have limitations and cannot achieve good generalization results. Therefore, a WCE‐DCGAN network was proposed in this paper to generate WCE images from existing WCE images. Using the images generated by this network and the original images as the input of the object detection network, there are different degrees of performance improvement on SSD, YOLOv5, and YOLOv4, and the average recognition accuracy of 97.25% can be achieved on SSD. Meanwhile, images generated by WCE‐DCGAN not only enlarge the size of the data set, but also have the characteristics of diversity, which makes the model have a good generalization effect. [ABSTRACT FROM AUTHOR]

Several factors, including advances in computational algorithms, the availability of high-performance computing hardware, and the assembly of large community-based databases, have led to the extensive application of Artificial Intelligence (AI) in the biomedical domain for nearly 20 years. AI algorithms have attained expert-level performance in cancer research. However, only a few AI-based applications have been approved for use in the real world. Whether AI will eventually be capable of replacing medical experts has been a hot topic. In this article, we first summarize the cancer research status using AI in the past two decades, including the consensus on the procedure of AI based on an ideal paradigm and current efforts of the expertise and domain knowledge. Next, the available data of AI process in the biomedical domain are surveyed. Then, we review the methods and applications of AI in cancer clinical research categorized by the data types including radiographic imaging, cancer genome, medical records, drug information and biomedical literatures. At last, we discuss challenges in moving AI from theoretical research to real-world cancer research applications and the perspectives toward the future realization of AI participating cancer treatment. [ABSTRACT FROM AUTHOR]