S5Utis: Structured State-Space Sequence SegNeXt UNet-like Tongue Image Segmentation in Traditional Chinese Medicine.

Intelligent Traditional Chinese Medicine can provide people with a convenient way to participate in daily health care. The ease of acceptance of Traditional Chinese Medicine is also a major advantage in promoting health management. In Traditional Chinese Medicine, tongue imaging is an important step in the examination process. The segmentation and processing of the tongue image directly affects the results of intelligent Traditional Chinese Medicine diagnosis. As intelligent Traditional Chinese Medicine continues to develop, remote diagnosis and patient participation will play important roles. Smartphone sensor cameras can provide irreplaceable data collection capabilities in enhancing interaction in smart Traditional Chinese Medicine. However, these factors lead to differences in the size and quality of the captured images due to factors such as differences in shooting equipment, professionalism of the photographer, and the subject's cooperation. Most current tongue image segmentation algorithms are based on data collected by professional tongue diagnosis instruments in standard environments, and are not able to demonstrate the tongue image segmentation effect in complex environments. Therefore, we propose a segmentation algorithm for tongue images collected in complex multi-device and multi-user environments. We use convolutional attention and extend state space models to the 2D environment in the encoder. Then, cross-layer connection fusion is used in the decoder part to fuse shallow texture and deep semantic features. Through segmentation experiments on tongue image datasets collected by patients and doctors in real-world settings, our algorithm significantly improves segmentation performance and accuracy.

High-Frequency Repetitive Transcranial Magnetic Stimulation and Its Effects on Symptoms, Cognition and Subjective Experiences in Chronic Schizophrenia: A Sham-Controlled Study.

Introduction: Our object is to examine the effects of high-frequency repetitive transcranial magnetic stimulation (rTMS) on the symptoms, cognitive functions and subjective experiences in patients with chronic schizophrenia and to enhance the overall understanding of the TMS method. Methods: Thirty three patients who had chronic schizophrenia were included in the study. Seventeen patients received rTMS and 16 received sham. The Positive and Negative Syndrome Scale, Repeatable Battery for the Assessment of Neuropsychological Status Scale, Insight and Treatment Attitudes Questionnaire and a self-experience checklist developed by the researchers to evaluate post-TMS experiences were applied to all patients. Results: There were no statistical differences between the groups with regard to symptoms, cognitive functions and insight. However rTMS group reported overall better treatment experience and more positive subjective experiences. Conclusion: rTMS treatment did not cause any improvement in symptoms, cognitive functions and insight but provided a better self-experience, which might improve treatment compliance.

Effects of bilateral repetitive transcranial magnetic stimulation on prospective memory in patients with schizophrenia: A double-blind randomized controlled clinical trial.

AIMS: To investigate effects of repetitive transcranial magnetic stimulation (rTMS) on the prospective memory (PM) in patients with schizophrenia (SCZ). METHODS: Fifty of 71 patients completed this double-blind placebo-controlled randomized trial and compared with 18 healthy controls' (HCs) PM outcomes. Bilateral 20 Hz rTMS to the dorsolateral prefrontal cortex at 90% RMT administered 5 weekdays for 4 weeks for a total of 20 treatments. The Positive and Negative Symptom Scale (PANSS), the Scale for the Assessment of Negative Symptoms (SANS), and PM test were assessed before and after treatment. RESULTS: Both Event-based PM (EBPM) and Time-based PM (TBPM) scores at baseline were significantly lower in patients with SCZ than that in HCs. After rTMS treatments, the scores of EBPM in patients with SCZ was significantly improved and had no differences from that in HCs, while the scores of TBPM did not improved. The negative symptom scores on PANSS and the scores of almost all subscales and total scores of SANS were significantly improved in both groups. CONCLUSIONS: Our findings indicated that bilateral high-frequency rTMS treatment can alleviate EBPM but not TBPM in patients with SCZ, as well as improve the negative symptoms. SIGNIFICANCE: Our results provide one therapeutic option for PM in patients with SCZ.

A retrospective case-control study of facial emotion recognition in male veterans with chronic schizophrenia and its correlation with interpersonal communication.

OBJECTIVE: To understand the facial emotion recognition of male veterans with chronic schizophrenia and the relationship between facial emotion recognition and interpersonal communication to provide a reference for designing social skills training programmes. METHOD: Fifty-six eligible male patients with chronic schizophrenia who were admitted to our hospital from October 2020 to April 2021 were selected, and 24 healthy people were selected as controls. Facial emotion recognition, social communication skills and self-perceived interpersonal disturbance were assessed using a facial emotion recognition stimulus manual, the Social Skills Checklist (SSC) and the Interpersonal Relationship Integrative Diagnostic Scale (IRIDS). Disease status was assessed using the Positive and Negative Syndrome Scale. RESULTS: Both the control group and the patient group had the highest recognition accuracy for neutral faces. The recognition rate for neutral expression was higher in the control group than in the patient group (p = 0.008). The rate of neutral expressions identified as happiness was higher in the patient group than in the control group (p = 0.001). The identification of anger as happiness was higher in the control group than in the patient group (p = 0.026), and the pattern of misidentification was similar between the control group and the patient group. The accuracy of facial emotion recognition was negatively associated with the age of onset (p < 0.05). The recognition accuracy for happiness was negatively associated with negative symptoms, general pathological symptoms and total scale scores (p < 0.05). The total score for expression recognition was negatively associated with the negative symptom subscale scores (p < 0.05), and there was no correlation between expression recognition and positive symptoms (p > 0.05). The recognition accuracy for happiness was negatively correlated with the IRIDS conversation factor (p < 0.05). The recognition accuracy for happiness and anger and the total scores for facial emotion recognition were negatively correlated with the SSC subscale score and the total score (p < 0.05 and p < 0.01, respectively). The main influencing factors on facial emotion recognition were the SSC total score (p < 0.001) and the positive factor score (p = 0.039). CONCLUSION: Veterans with chronic schizophrenia have facial emotion recognition impairments affected by negative symptoms. There is a correlation between facial emotion recognition and interpersonal communication. HIGHLIGHTS: 1. There are extensive facial expression recognition disorders in schizophrenia. 2. The pattern of misidentification was similar in both the control group and the patient group, with the tendency for happiness to be identified as a neutral emotion, anger as happiness, and fear as neutral emotion and anger. 3. Based on the comprehensive assessment of social cognitive impairment in patients with schizophrenia, prospective studies of standardised interventions are designed to provide support for clinical practice.

Relative Distribution Entropy Loss Function in CNN Image Retrieval.

Convolutional neural networks (CNN) is the most mainstream solution in the field of image retrieval. Deep metric learning is introduced into the field of image retrieval, focusing on the construction of pair-based loss function. However, most pair-based loss functions of metric learning merely take common vector similarity (such as Euclidean distance) of the final image descriptors into consideration, while neglecting other distribution characters of these descriptors. In this work, we propose relative distribution entropy (RDE) to describe the internal distribution attributes of image descriptors. We combine relative distribution entropy with the Euclidean distance to obtain the relative distribution entropy weighted distance (RDE-distance). Moreover, the RDE-distance is fused with the contrastive loss and triplet loss to build the relative distributed entropy loss functions. The experimental results demonstrate that our method attains the state-of-the-art performance on most image retrieval benchmarks.

Optic Disc Segmentation Using Attention-Based U-Net and the Improved Cross-Entropy Convolutional Neural Network.

Medical image segmentation is an important part of medical image analysis. With the rapid development of convolutional neural networks in image processing, deep learning methods have achieved great success in the field of medical image processing. Deep learning is also used in the field of auxiliary diagnosis of glaucoma, and the effective segmentation of the optic disc area plays an important assistant role in the diagnosis of doctors in the clinical diagnosis of glaucoma. Previously, many U-Net-based optic disc segmentation methods have been proposed. However, the channel dependence of different levels of features is ignored. The performance of fundus image segmentation in small areas is not satisfactory. In this paper, we propose a new aggregation channel attention network to make full use of the influence of context information on semantic segmentation. Different from the existing attention mechanism, we exploit channel dependencies and integrate information of different scales into the attention mechanism. At the same time, we improved the basic classification framework based on cross entropy, combined the dice coefficient and cross entropy, and balanced the contribution of dice coefficients and cross entropy loss to the segmentation task, which enhanced the performance of the network in small area segmentation. The network retains more image features, restores the significant features more accurately, and further improves the segmentation performance of medical images. We apply it to the fundus optic disc segmentation task. We demonstrate the segmentation performance of the model on the Messidor dataset and the RIM-ONE dataset, and evaluate the proposed architecture. Experimental results show that our network architecture improves the prediction performance of the base architectures under different datasets while maintaining the computational efficiency. The results render that the proposed technologies improve the segmentation with 0.0469 overlapping error on Messidor.