Reconstruction of Knowledge and Medical Images in the Convergence of Chinese and Western Medicine:Taking “Sweet Meat” as an Example

The pancreas is neither part of the five Zang organs(五脏) nor the six Fu organs(六腑).Thus,it has received little attention in Chinese medical literature.In the late 19th century,medical missionaries in China started translating and introducing anatomical and physiological knowledge about the pancreas.As for the word pancreas,an early and influential translation was “sweet meat”(甜肉),proposed by Benjamin Hobson(合信).The translation “sweet meat” is not faithful to the original meaning of “pancreas”,but is a term coined by Hobson based on his personal habits,and the word “sweet” appeared by chance.However,in the decades since the term “sweet meat” became popular,Chinese medicine practitioners,such as Tang Zonghai(唐宗海),reinterpreted it by drawing new medical illustrations for “sweet meat” and giving new connotations to the word “sweet”.This discussion and interpretation of “sweet meat” in modern China,particularly among Chinese medicine professionals,is not only a dissemination and interpretation of the knowledge of “pancreas”,but also a construction of knowledge around the term “sweet meat”.

Ethics of Human Resources Management in the Cameroonian Health System, Medical Nomadism and the Ineffectiveness of the Fight against High Blood Pressure

Context/objectives: The fight against Chronic Non-Communicable Diseases (NCDs) is a long-term undertaking, which requires available, motivated and well-managed human resources (HR). The administrative management of skills on both qualitative and quantitative levels is one of the essential functions of a health system. To better implement policies of fight against High Blood Pressure (HBP) and other chronic diseases, it is important to establish strategies to retain health personnel. This loyalty requires favorable working conditions and consideration of the contribution-reward couple. Good working conditions are likely to reduce the phenomenon of medical nomadism;conversely, poor HR management can contribute to their exodus towards exotic “green pastures”, thus leading to an additional crisis in the Cameroonian health system. The fight against HBP is a complex, multifaceted and multifactorial reality that requires appropriate management model for all types of resources mainly HR. The main objective of this research is to show the impact of poor management of human resources in Cameroon health system on medical nomadism and the ineffectiveness of the fight against High Blood Pressure. Method: A cross-sectional descriptive survey among five hundred (500) health facilities in the center region of Cameroon has been conducted. A stratified probabilistic technique has been used, and the number of health facilities to be surveyed has been determined using the “sample size estimation table” of Depelteau. The physical questionnaires have been printed and then distributed to data collectors. After data collection, the latter were grouped during processing in Excel sheets. The Chi-square test was used for data with a qualitative value and that of Kolmogorov-Sminorf for data with a quantitative value to assess the normality and reliability of data. The Crochach’s Alpha reliability test allowed us to have a summary of the means and variances and then to search for intragroup correlations between variables. Descriptive analysis was possible with the XLSTAT 2016 software. Results: 43.60% of Health Facilities (HF) managers were unqualified. 82.20% of HF managers have staff in a situation of professional insecurity. They are mainly contractual (49.00), decision-making agents (24.40%), casual agents (08.80). The proportion of unstable personnel is average of 22.00% and very unstable, 12.00%.

Research on the Application and Effectiveness of the DRG Model on Perioperative Medical Management of Cholecystectomy

Objective:This study aimed to explore the application and effectiveness of the DRG model in the perioperative management of cholecystectomy.By comparing the DRG model with traditional management methods,this study focused on evaluating the potential impact of the DRG model in improving surgical efficiency and reducing complication rates and medical costs.Methods:The random envelope method was used to divide patients scheduled for cholecystectomy from January 2021 to October 2023 into two groups:one group underwent surgery under the DRG model(experimental group),and the other group underwent the traditional management model(control group).Data including basic information,surgery-related data,length of stay,complication records,and medical expenses were collected.Data analysis was carried out using a t-test and chi-square(χ2)test.Results:Results showed that the DRG model shortened the average length of stay,decreased the incidence of complications,reduced medical expenses,and increased patient satisfaction.These results demonstrate the effectiveness of the DRG model in the perioperative management of cholecystectomy,especially in improving surgical efficiency,reducing medical costs,and improving patient satisfaction.Conclusion:The DRG model in the perioperative management of cholecystectomy can significantly improve medical service quality and efficiency and enhance patient satisfaction as compared to traditional treatment methods.

Evaluation of Medical Prescribers’ Theoretical Knowledge on Medical Imaging in the Northern Region of Burkina Faso

Introduction: Medical imaging is a medical specialty that involves producing images of the human body and interpreting them for diagnostic, therapeutic purposes, and for monitoring the progress of pathologies. We aimed to assess the theoretical knowledge of doctors and interns in medical imaging in the northern region of Burkina Faso. Methodology: This was a descriptive cross-sectional survey based on a self-administered questionnaire. Prescribers knowledge was estimated based on scores derived from questionnaire responses. Results: We collected 106 questionnaires out of 163, i.e. a participation rate of 65.03%. The average knowledge score was 81.71% for the contribution of medical imaging to patient management. It was 60.02% for the indications/counter-indications of radiological examinations and 72.56% for the risks associated with exposure to radiation during these examinations. The score was 59.83% for the methods used to select the appropriate radiological examination. As regards the completeness of the clinical and biological information on the forms requesting imaging examinations, the score was 96.65%. Specialist doctors had the highest overall level of knowledge (74.68%). Conclusion: Improved technical facilities, good initial and in-service training, and interdisciplinary collaboration will help to ensure that imaging tests are properly prescribed, leading to better patient care.

Application and Effect Evaluation of the Integrated“5A and 3+3”Management Model in the Safe Medication Use for Chemotherapy Patients

Objective:To explore the application and effect evaluation of the integrated“5A and 3+3”management model in ensuring safe medication use for chemotherapy patients.Methods:A total of 100 intravenous chemotherapy patients admitted to the oncology department of Shaanxi Provincial People’s Hospital were randomly divided into two groups using a random number list method.Both groups received conventional nursing management during chemotherapy,while the study group additionally received the integrated“5A and 3+3”safety management model.The nursing intervention effects between the two groups were compared.Results:After the intervention,the study group showed higher levels of self-management ability,compliance,and nursing satisfaction compared to the control group.The overall incidence of adverse events during hospitalization was lower in the study group,with statistically significant differences(P<0.05).The knowledge scores of medical staff in the study group,related to the prevention and treatment of chemotherapy drug side effects,daily symptom management,and daily life management,were higher than those in the control group,with statistically significant differences(P<0.05).Conclusion:Implementing the integrated“5A and 3+3”model in the safe medication management of intravenous chemotherapy patients can effectively enhance patients’self-management abilities and compliance,improve medical staff’s ability to safely administer chemotherapy drugs,reduce adverse events caused by chemotherapy,and increase patient satisfaction.

The Application Value of Sanitary Wipes and Medical Alcohol+Gauze Disinfection Method in the Management and Use of Medical Equipment

Objective:To analyze the effect of sanitary wipes and medical alcohol+gauze disinfection methods on the management and use of medical equipment in hospitals.Methods:From February 2023 to August 2023,80 anorectal surgery medical instruments and equipment were randomly sampled as observation samples,and they were grouped using the random number table method(40 instruments in each group).One group was managed with sanitary wipes and medical alcohol+gauze disinfection method(observation group),and the other group was managed with medical alcohol+gauze disinfection method(control group).The degree of contamination on the surface of instruments and equipment and the staff satisfaction of both groups was noted and compared.Results:After cleaning and disinfection,the relative luminescence luminosity(RLU)value of the surface contamination of the sample instruments and equipment decreased significantly(P<0.05);and after cleaning and disinfection,the relative luminescence luminosity(RLU)of the observation group’s instruments and equipment decreased significantly,and the value was lower than that of the control group,(P<0.05);the results of the nursing job satisfaction questionnaire showed that the scores of each survey item in the observation group were higher than those in the control group,(P<0.05).Conclusion:Integrating sanitary wipes and medical alcohol+gauze disinfection method into the management and use of medical equipment not only improves the disinfection of medical instruments but also improves the job satisfaction of nursing staff.Therefore,this method should be popularized.

Fractional-order heterogeneous memristive Rulkov neuronal network and its medical image watermarking application

This article proposes a novel fractional heterogeneous neural network by coupling a Rulkov neuron with a Hopfield neural network(FRHNN),utilizing memristors for emulating neural synapses.The study firstly demonstrates the coexistence of multiple firing patterns through phase diagrams,Lyapunov exponents(LEs),and bifurcation diagrams.Secondly,the parameter related firing behaviors are described through two-parameter bifurcation diagrams.Subsequently,local attraction basins reveal multi-stability phenomena related to initial values.Moreover,the proposed model is implemented on a microcomputer-based ARM platform,and the experimental results correspond to the numerical simulations.Finally,the article explores the application of digital watermarking for medical images,illustrating its features of excellent imperceptibility,extensive key space,and robustness against attacks including noise and cropping.

Research on Multi-Scale Feature Fusion Network Algorithm Based on Brain Tumor Medical Image Classification

Gliomas have the highest mortality rate of all brain tumors.Correctly classifying the glioma risk period can help doctors make reasonable treatment plans and improve patients’survival rates.This paper proposes a hierarchical multi-scale attention feature fusion medical image classification network(HMAC-Net),which effectively combines global features and local features.The network framework consists of three parallel layers:The global feature extraction layer,the local feature extraction layer,and the multi-scale feature fusion layer.A linear sparse attention mechanism is designed in the global feature extraction layer to reduce information redundancy.In the local feature extraction layer,a bilateral local attention mechanism is introduced to improve the extraction of relevant information between adjacent slices.In the multi-scale feature fusion layer,a channel fusion block combining convolutional attention mechanism and residual inverse multi-layer perceptron is proposed to prevent gradient disappearance and network degradation and improve feature representation capability.The double-branch iterative multi-scale classification block is used to improve the classification performance.On the brain glioma risk grading dataset,the results of the ablation experiment and comparison experiment show that the proposed HMAC-Net has the best performance in both qualitative analysis of heat maps and quantitative analysis of evaluation indicators.On the dataset of skin cancer classification,the generalization experiment results show that the proposed HMAC-Net has a good generalization effect.

Nonlinear Registration of Brain Magnetic Resonance Images with Cross Constraints of Intensity and Structure

Many deep learning-based registration methods rely on a single-stream encoder-decoder network for computing deformation fields between 3D volumes.However,these methods often lack constraint information and overlook semantic consistency,limiting their performance.To address these issues,we present a novel approach for medical image registration called theDual-VoxelMorph,featuring a dual-channel cross-constraint network.This innovative network utilizes both intensity and segmentation images,which share identical semantic information and feature representations.Two encoder-decoder structures calculate deformation fields for intensity and segmentation images,as generated by the dual-channel cross-constraint network.This design facilitates bidirectional communication between grayscale and segmentation information,enabling the model to better learn the corresponding grayscale and segmentation details of the same anatomical structures.To ensure semantic and directional consistency,we introduce constraints and apply the cosine similarity function to enhance semantic consistency.Evaluation on four public datasets demonstrates superior performance compared to the baselinemethod,achieving Dice scores of 79.9%,64.5%,69.9%,and 63.5%for OASIS-1,OASIS-3,LPBA40,and ADNI,respectively.

A review of medical ocular image segmentation

Deep learning has been extensively applied to medical image segmentation,resulting in significant advancements in the field of deep neural networks for medical image segmentation since the notable success of U Net in 2015.However,the application of deep learning models to ocular medical image segmentation poses unique challenges,especially compared to other body parts,due to the complexity,small size,and blurriness of such images,coupled with the scarcity of data.This article aims to provide a comprehensive review of medical image segmentation from two perspectives:the development of deep network structures and the application of segmentation in ocular imaging.Initially,the article introduces an overview of medical imaging,data processing,and performance evaluation metrics.Subsequently,it analyzes recent developments in U-Net-based network structures.Finally,for the segmentation of ocular medical images,the application of deep learning is reviewed and categorized by the type of ocular tissue.

DCFNet:An Effective Dual-Branch Cross-Attention Fusion Network for Medical Image Segmentation

Automatic segmentation of medical images provides a reliable scientific basis for disease diagnosis and analysis.Notably,most existing methods that combine the strengths of convolutional neural networks(CNNs)and Transformers have made significant progress.However,there are some limitations in the current integration of CNN and Transformer technology in two key aspects.Firstly,most methods either overlook or fail to fully incorporate the complementary nature between local and global features.Secondly,the significance of integrating the multiscale encoder features from the dual-branch network to enhance the decoding features is often disregarded in methods that combine CNN and Transformer.To address this issue,we present a groundbreaking dual-branch cross-attention fusion network(DCFNet),which efficiently combines the power of Swin Transformer and CNN to generate complementary global and local features.We then designed the Feature Cross-Fusion(FCF)module to efficiently fuse local and global features.In the FCF,the utilization of the Channel-wise Cross-fusion Transformer(CCT)serves the purpose of aggregatingmulti-scale features,and the Feature FusionModule(FFM)is employed to effectively aggregate dual-branch prominent feature regions from the spatial perspective.Furthermore,within the decoding phase of the dual-branch network,our proposed Channel Attention Block(CAB)aims to emphasize the significance of the channel features between the up-sampled features and the features generated by the FCFmodule to enhance the details of the decoding.Experimental results demonstrate that DCFNet exhibits enhanced accuracy in segmentation performance.Compared to other state-of-the-art(SOTA)methods,our segmentation framework exhibits a superior level of competitiveness.DCFNet’s accurate segmentation of medical images can greatly assist medical professionals in making crucial diagnoses of lesion areas in advance.

Contribution of CT Scanning in the Investigation of Cranioencephalic Lesions in Bangui

Introduction: Cranioencephalic exploration has always played a major role in CT scans. In the Central Africa Republic (CAR), the lack of cross-sectional imaging before the year 2020 meant that no study had focused on cranioencephalic lesions. The aim of this study was to contribute to improving the management of cranioencephalic pathologies in CAR. Patients and Method: The study took place at the Bangui National Medical Imaging Centre (CNIMB). It was a retrospective study over a two-year period (March 1, 2021 to February 30, 2023). All patients referred for cranioencephalic CT scans were included, regardless of age or sex. Results: 1745 CT scans were performed, 575 of which were cranioencephalic CT scans. The majority of patients were male (53%). Most lived in the capital Bangui (90.9%). Patients aged 61 and over were the most representative. The distribution of patients by requesting department showed that the reception and emergency department was one of the least requesting departments. The main abnormalities observed were strokes, 82.1% of which were ischaemic strokes and 17.9% haemorrhagic strokes. Strokes were followed by degenerative lesions. Post-traumatic injuries included haemorrhagic contusions (38.3%), subdural haematomas in 20.5% of cases, and extradural haematomas (9.3%). Craniofacial lesions (fractures) were observed in 45.8% of cases. Conclusion: Cranioencephalic scans accounted for 1/3 of CT examinations performed during the study period. It revealed pathologies that could not be detected by conventional means. All in all, CT scans contributed to the diagnosis of cerebral pathologies.

Global Memory Net and the development of digital image information management system: Experience and practice

Global Memory Net (GMNet) is intended to be an effective gateway to the world cultural, historical, and heritage image collections from selected academic educational and research partners in the world. Much of these unique collections of great value to education and research are not currently accessible due to distance, form, and technical barriers. This project is to find new ways to enable users to access and exploit these significant research collections via global network. As GMNet is ending its first 5-year phase in October 2005, it has contributed substantially to the community building in digital library development by ac- commodating numerous collaborators and technical staff from various parts of the world to spend 3 to 5 months as a full-member of the GMNet team in Boston. They have come from different parts of China—such as Sichuan, Hainan, Shanghai and Xi’an; Croatia; and Hanoi, Vietnam. In addition to contribute to the overall system development and enhancement of system function- alities, they have brought valuable sample image collections of their own institutions/countries, and actually developed prototype collections as a part of GMNet. This paper describes the exciting and productive experience of the first of this visiting research group in developing the GMNet’s Version 2.0 PHP-based system under Prof. Chen’s overall supervision. It also describes both the system’s technical level structure—user/Web-based application/data, and complex functionalities with multi-collection, multi-lingual, multi-modal searching capabilities; system management capabilities; as well as provisions for user uploads and retrieval for our own projects. This Version 2.0 system is built on the Linux/Apache/PHP/MySQL platform. What is described in this paper is an actual case which has formed a base for further new development by others in the research group. It demonstrates fully the value of the synergistic collaboration among global partners for universal digital library development. More information can be found in http://www.memorynet.org/.

Reform and Implementation of Teaching Management for Medical Graduate Students

With the expansion of medical graduate enrollment, there are some problems in graduate teaching management, such as shortage of faculty, insufficient funding, and single assessment management. This can be solved by improving the quality of enrollment, strengthening process management and quality monitoring, enhancing the quality of mentors, and building a management team. This provides theoretical support and practical guidance for the management of graduate education in medical colleges, which helps to improve the quality of graduate education and cultivate more high-quality medical talents for the national healthcare industry.

Application of U-Net and Optimized Clustering in Medical Image Segmentation:A Review

As a mainstream research direction in the field of image segmentation,medical image segmentation plays a key role in the quantification of lesions,three-dimensional reconstruction,region of interest extraction and so on.Compared with natural images,medical images have a variety of modes.Besides,the emphasis of information which is conveyed by images of different modes is quite different.Because it is time-consuming and inefficient to manually segment medical images only by professional and experienced doctors.Therefore,large quantities of automated medical image segmentation methods have been developed.However,until now,researchers have not developed a universal method for all types of medical image segmentation.This paper reviews the literature on segmentation techniques that have produced major breakthroughs in recent years.Among the large quantities of medical image segmentation methods,this paper mainly discusses two categories of medical image segmentation methods.One is the improved strategies based on traditional clustering method.The other is the research progress of the improved image segmentation network structure model based on U-Net.The power of technology proves that the performance of the deep learning-based method is significantly better than that of the traditional method.This paper discussed both advantages and disadvantages of different algorithms and detailed how these methods can be used for the segmentation of lesions or other organs and tissues,as well as possible technical trends for future work.

Designing a High-Performance Deep Learning Theoretical Model for Biomedical Image Segmentation by Using Key Elements of the Latest U-Net-Based Architectures

Deep learning (DL) has experienced an exponential development in recent years, with major impact in many medical fields, especially in the field of medical image and, respectively, as a specific task, in the segmentation of the medical image. We aim to create a computer assisted diagnostic method, optimized by the use of deep learning (DL) and validated by a randomized controlled clinical trial, is a highly automated tool for diagnosing and staging precancerous and cervical cancer and thyroid cancers. We aim to design a high-performance deep learning model, combined from convolutional neural network (U-Net)-based architectures, for segmentation of the medical image that is independent of the type of organs/tissues, dimensions or type of image (2D/3D) and to validate the DL model in a randomized, controlled clinical trial. We used as a methodology primarily the analysis of U-Net-based architectures to identify the key elements that we considered important in the design and optimization of the combined DL model, from the U-Net-based architectures, imagined by us. Secondly, we will validate the performance of the DL model through a randomized controlled clinical trial. The DL model designed by us will be a highly automated tool for diagnosing and staging precancers and cervical cancer and thyroid cancers. The combined model we designed takes into account the key features of each of the architectures Overcomplete Convolutional Network Kite-Net (Kite-Net), Attention gate mechanism is an improvement added on convolutional network architecture for fast and precise segmentation of images (Attention U-Net), Harmony Densely Connected Network-Medical image Segmentation (HarDNet-MSEG). In this regard, we will create a comprehensive computer assisted diagnostic methodology validated by a randomized controlled clinical trial. The model will be a highly automated tool for diagnosing and staging precancers and cervical cancer and thyroid cancers. This would help drastically minimize the time and effort that specialists put into analyzing medical images, help to achieve a better therapeutic plan, and can provide a “second opinion” of computer assisted diagnosis.

Image Color Rendering Based on Hinge-Cross-Entropy GAN in Internet of Medical Things

Computer-aided diagnosis based on image color rendering promotes medical image analysis and doctor-patient communication by highlighting important information of medical diagnosis.To overcome the limitations of the color rendering method based on deep learning,such as poor model stability,poor rendering quality,fuzzy boundaries and crossed color boundaries,we propose a novel hinge-cross-entropy generative adversarial network(HCEGAN).The self-attention mechanism was added and improved to focus on the important information of the image.And the hinge-cross-entropy loss function was used to stabilize the training process of GAN models.In this study,we implement the HCEGAN model for image color rendering based on DIV2K and COCO datasets,and evaluate the results using SSIM and PSNR.The experimental results show that the proposed HCEGAN automatically re-renders images,significantly improves the quality of color rendering and greatly improves the stability of prior GAN models.

A Novel Unsupervised MRI Synthetic CT Image Generation Framework with Registration Network

In recent years,radiotherapy based only on Magnetic Resonance(MR)images has become a hot spot for radiotherapy planning research in the current medical field.However,functional computed tomography(CT)is still needed for dose calculation in the clinic.Recent deep-learning approaches to synthesized CT images from MR images have raised much research interest,making radiotherapy based only on MR images possible.In this paper,we proposed a novel unsupervised image synthesis framework with registration networks.This paper aims to enforce the constraints between the reconstructed image and the input image by registering the reconstructed image with the input image and registering the cycle-consistent image with the input image.Furthermore,this paper added ConvNeXt blocks to the network and used large kernel convolutional layers to improve the network’s ability to extract features.This research used the collected head and neck data of 180 patients with nasopharyngeal carcinoma to experiment and evaluate the training model with four evaluation metrics.At the same time,this research made a quantitative comparison of several commonly used model frameworks.We evaluate the model performance in four evaluation metrics which achieve Mean Absolute Error(MAE),Root Mean Square Error(RMSE),Peak Signal-to-Noise Ratio(PSNR),and Structural Similarity(SSIM)are 18.55±1.44,86.91±4.31,33.45±0.74 and 0.960±0.005,respectively.Compared with other methods,MAE decreased by 2.17,RMSE decreased by 7.82,PSNR increased by 0.76,and SSIM increased by 0.011.The results show that the model proposed in this paper outperforms other methods in the quality of image synthesis.The work in this paper is of guiding significance to the study of MR-only radiotherapy planning.

Application of Zero-Watermarking for Medical Image in Intelligent Sensor Network Security

The field of healthcare is considered to be the most promising application of intelligent sensor networks.However,the security and privacy protection ofmedical images collected by intelligent sensor networks is a hot problem that has attracted more and more attention.Fortunately,digital watermarking provides an effective method to solve this problem.In order to improve the robustness of the medical image watermarking scheme,in this paper,we propose a novel zero-watermarking algorithm with the integer wavelet transform(IWT),Schur decomposition and image block energy.Specifically,we first use IWT to extract low-frequency information and divide them into non-overlapping blocks,then we decompose the sub-blocks by Schur decomposition.After that,the feature matrix is constructed according to the relationship between the image block energy and the whole image energy.At the same time,we encrypt watermarking with the logistic chaotic position scrambling.Finally,the zero-watermarking is obtained by XOR operation with the encrypted watermarking.Three indexes of peak signal-to-noise ratio,normalization coefficient(NC)and the bit error rate(BER)are used to evaluate the robustness of the algorithm.According to the experimental results,most of the NC values are around 0.9 under various attacks,while the BER values are very close to 0.These experimental results show that the proposed algorithm is more robust than the existing zero-watermarking methods,which indicates it is more suitable for medical image privacy and security protection.

Short‐term and long‐term memory self‐attention network for segmentation of tumours in 3D medical images

Tumour segmentation in medical images(especially 3D tumour segmentation)is highly challenging due to the possible similarity between tumours and adjacent tissues,occurrence of multiple tumours and variable tumour shapes and sizes.The popular deep learning‐based segmentation algorithms generally rely on the convolutional neural network(CNN)and Transformer.The former cannot extract the global image features effectively while the latter lacks the inductive bias and involves the complicated computation for 3D volume data.The existing hybrid CNN‐Transformer network can only provide the limited performance improvement or even poorer segmentation performance than the pure CNN.To address these issues,a short‐term and long‐term memory self‐attention network is proposed.Firstly,a distinctive self‐attention block uses the Transformer to explore the correlation among the region features at different levels extracted by the CNN.Then,the memory structure filters and combines the above information to exclude the similar regions and detect the multiple tumours.Finally,the multi‐layer reconstruction blocks will predict the tumour boundaries.Experimental results demonstrate that our method outperforms other methods in terms of subjective visual and quantitative evaluation.Compared with the most competitive method,the proposed method provides Dice(82.4%vs.76.6%)and Hausdorff distance 95%(HD95)(10.66 vs.11.54 mm)on the KiTS19 as well as Dice(80.2%vs.78.4%)and HD95(9.632 vs.12.17 mm)on the LiTS.