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White light cystoscopy is the gold standard for the diagnosis of bladder cancer. Automatic and accurate tumor detection is essential to improve the surgical resection of bladder cancer and reduce tumor recurrence. At present, Transformer-based medical image segmentation algorithms face challenges in restoring fine-grained detail information and local boundary information of features and have limited adaptability to multi-scale features of lesions. To address these issues, we propose a new multi-scale detail-enhanced reverse attention network, MDER-Net, for accurate and robust bladder tumor segmentation. Firstly, we propose a new multi-scale efficient channel attention module (MECA) to process four different levels of features extracted by the PVT v2 encoder to adapt to the multi-scale changes in bladder tumors; secondly, we use the dense aggregation module (DA) to aggregate multi-scale advanced semantic feature information; then, the similarity aggregation module (SAM) is used to fuse multi-scale high-level and low-level features, complementing each other in position and detail information; finally, we propose a new detail-enhanced reverse attention module (DERA) to capture non-salient boundary features and gradually explore supplementing tumor boundary feature information and fine-grained detail information; in addition, we propose a new efficient channel space attention module (ECSA) that enhances local context and improves segmentation performance by suppressing redundant information in low-level features. Extensive experiments on the bladder tumor dataset BtAMU, established in this article, and five publicly available polyp datasets show that MDER-Net outperforms eight state-of-the-art (SOTA) methods in terms of effectiveness, robustness, and generalization ability. [ABSTRACT FROM AUTHOR]

Emotion recognition utilizing EEG signals has emerged as a pivotal component of human–computer interaction. In recent years, with the relentless advancement of deep learning techniques, using deep learning for analyzing EEG signals has assumed a prominent role in emotion recognition. Applying deep learning in the context of EEG-based emotion recognition carries profound practical implications. Although many model approaches and some review articles have scrutinized this domain, they have yet to undergo a comprehensive and precise classification and summarization process. The existing classifications are somewhat coarse, with insufficient attention given to the potential applications within this domain. Therefore, this article systematically classifies recent developments in EEG-based emotion recognition, providing researchers with a lucid understanding of this field's various trajectories and methodologies. Additionally, it elucidates why distinct directions necessitate distinct modeling approaches. In conclusion, this article synthesizes and dissects the practical significance of EEG signals in emotion recognition, emphasizing its promising avenues for future application. [ABSTRACT FROM AUTHOR]

In the wake of the incessant evolution of virtual reality (VR) technology, its pervasive integration into an expanding array of sectors, notably the medical field, has become increasingly conspicuous. Amid these developments, post‐traumatic stress disorder (PTSD), a prevalent and debilitating mental health condition, has garnered heightened attention from the medical community. The contemporary therapeutic landscape predominantly revolves around psychological interventions, exemplified by exposure therapy and positive thinking therapy. However, a groundbreaking advancement, namely virtual reality exposure therapy (VRET), has emerged as a prominent achievement, effectively intertwining VR technology with medical applications, particularly in the nuanced treatment of PTSD. As the realms of human‐computer interaction and brain‐computer interface undergo continuous and dynamic expansion, electroencephalography (EEG) has surfaced as a pivotal convergence point across diverse disciplines. The confluence of VRET technology with EEG signals holds the promise of enhancing the precision and objectivity of patient diagnoses. Furthermore, leveraging EEG signals enables a more nuanced and individualized approach to treatment within the framework of VRET technology. Despite extant studies that offer comprehensive summaries of VRET technology, the integration with EEG signals has thus far been a relatively unexplored terrain. This research endeavors to direct attention toward recent advancements in VRET technology within the sphere of mental health treatment. A meticulous synthesis and exposition of existing techniques and applications are presented, providing a comprehensive overview of the current landscape. The exploration extends to the developmental trajectory and prospective implications arising from the amalgamation of VRET technology and EEG technology. Furthermore, a rigorous analysis of the tangible contributions and practical significance of EEG signals in the domain of mental health treatment is undertaken, underscoring the potential avenues for their future applications and the transformative impact on the trajectory of therapeutic interventions. [ABSTRACT FROM AUTHOR]

Anxiety and stress pose significant mental health challenges in modern society, with traditional exposure therapy showing limitations, necessitating the quest for more effective interventions. Virtual reality (VR) technology has emerged as a promising tool for anxiety and stress management. This systematic review examines VR's application in anxiety and stress management from 2000 to 2023, drawing from three electronic databases: IEEE Xplore, ACM Digital Library, and ScienceDirect. The review highlights VR's potential in anxiety and stress management, particularly through virtual reality exposure therapy (VRET), relaxation training (RT), and virtual‐reality‐based cognitive behavioral therapy (VR‐CBT). These approaches have demonstrated effectiveness in reducing anxiety levels and improving relaxation skills. Despite its promise, VR's application faces challenges such as high technical costs, device convenience, validation of long‐term effects, and personalized treatment plans. Further research is needed to assess VR therapy's long‐term effects and application value comprehensively. Future directions for VR in anxiety and stress management include the development of more intelligent and personalized treatment plans, leveraging artificial intelligence (AI) technology. Large‐scale clinical trials and long‐term follow‐up studies are essential to validate VR technology's efficacy and support its widespread application in mental health care. [ABSTRACT FROM AUTHOR]