Specialized Intelligent Processing Methods For Medical Data

In recent years,with the continuous advancement of the healthcare sector,medical diagnostic techniques have been steadily improving.One notable aspect of this progress is the growing diversity of medical data.Ranging from traditional clinical records to state-of-the-art medical imaging,this diversification has enriched the healthcare domain with additional dimensions,allowing healthcare professionals to gain a more comprehensive insight into a patient’s physical condition.This diverse dataset provides a robust foundation for personalized medicine and precision healthcare.However,the diversity within medical data has also amplified the complexity of its analysis.Different types of medical data possess unique data structures and medical characteristics,often exhibiting a degree of variability,necessitating precise data preprocessing and quality control.In this context,specialized research into methods tailored for handling diverse medical data becomes increasingly significant.Furthermore,the diversity and multimodality of medical data,in conjunction with the continual emergence of novel examination technologies and data types,have resulted in the vastness of medical data.Confronted with abundant and diverse medical big data,artificial intelligence methods demonstrate a marked advantage in addressing complex medical data analysis challenges when compared to traditional machine learning techniques,which heavily depend on feature engineering.With the rise of the artificial intelligence era and the increased level of informatization in the medical field,deep neural networks have become the predominant approach in intelligent medicine.Intelligent medicine seeks to fuse domain-specific medical knowledge with the design of specialized artificial intelligence methods customized for the unique characteristics of various medical data types.Its focus is on research into artificial intelligence processing and analysis methods tailored to real medical scenarios,thus enhancing the reliability and accuracy of computer-aided clinical diagnosis.Specialized artificial intelligence processing for medical data carries great significance in deepening our understanding of and effectively utilizing medical data,with the potential to contribute positively to the advancement of the medical field and the well-being of patients in the future.The article delves into the intricacies of medical data,conducting a comprehensive analysis and exploration of existing deep neural network models,with a specific focus on developing specialized intelligent processing methods for medical data in diverse healthcare scenarios.It begins by tackling the complex analytical challenges that emerge during the diagnostic process,attributed to variables such as varying monitoring durations,nursing procedures,and the influence of environmental noise.This primary focus revolves around the diagnosis of neonatal amplitude-integrated electroencephalogram(a EEG).The research is centered on intelligent analysis methods for neonatal a EEG,based on non-standardized EEG monitoring and the transfer of clinical diagnostic expertise.Subsequently,the article addresses the formidable task of detecting and segmenting minor target lesions in medical images,which are characterized by a wealth of information.The research centers on the detection and segmentation of lower abdominal lymph nodes in full abdominal computed tomography(CT)scans,making use of clinical domain knowledge and the dual attention mechanism.The article then contends with the inherent diversity among various types of medical images,with a particular focus on the precise segmentation of two common medical image types.It delves into intelligent segmentation algorithms for the target regions in medical images,considering their inherent shape constraints.Lastly,the article ventures into the realm of multimodal processing for a single type of medical data,with a specific emphasis on the discriminative diagnosis of heart sound data.It introduces an intelligent classification method for heart sounds that integrates memory and multimodal information.In summary,the innovations and principal contributions of this article can be succinctly summarized as follows:1.Addressing the issues arising from irregular waveform data collection and diverse patterns,two distinct models have been proposed.The neonatal a EEG discriminative model,which incorporates attention transfer and feature integrators,aims to enhance model accuracy in the face of non-standardized data collection.Likewise,the multimodal heart sound classification model incorporates a memory fusion module to elevate accuracy for both tasks.In the context of real clinical data acquisition,the automated analysis of medical data often encounters numerous challenges.Using neonatal a EEG and heart sound data as examples,the data collection process is inevitably influenced by environmental and physiological factors.To address this,the paper presents specialized intelligent analysis methods.The neonatal a EEG,in its simplified form,reflects the developmental status of a newborn’s brain.However,in real clinical monitoring scenarios,the monitoring duration varies,and the monitored content is affected by nursing procedures,resulting in increased complexity when interpreting neonatal a EEG comprehensively.To tackle this,our paper employs an adaptive step strategy to normalize neonatal a EEG data.We introduce weight factors to mathematically transfer the focus of clinical diagnostic expertise.Our research introduces a four-stage discriminative model,encompassing feature enhancement,extraction,integration,and classification.Additionally,we design specialized feature integrators for comprehensive intelligent analysis of a EEG data.By incorporating the transfer of clinical diagnostic expertise into the model’s computations,we mitigate interference from external factors and achieve higher accuracy.To evaluate our method’s performance,we construct a clinical neonatal a EEG dataset with image-level labels,demonstrating that our approach effectively enhances diagnostic efficiency and accuracy.Similarly,heart sound audio provides early support for diagnosing related diseases.However,the collection of heart sound data is susceptible to interference from environmental and physiological factors.Given that heart sound data is in the form of time-series data,it necessitates comprehensive analysis of time-frequency features and more.Our paper standardizes raw heart sound data and subsequently extracts multimodal feature representations such as spectral characteristics and energy profiles,enriching the detail in heart sound audio data.To further extract local features of different dimensions and capture spatiotemporal correlations in contextual information among different modalities,we employ a bidirectional long short-term memory module with an attention mechanism and a Transformer structure.The method proposed in our paper achieves high accuracy on three public heart sound datasets,potentially expediting the clinical diagnosis process in the future.2.Addressing the complexities of abdominal structures and the challenge of detecting tiny target lesions,this paper introduces a cascaded lymph node detection and partition method based on spatial prior knowledge.Furthermore,it implements the dual attention mechanism to enhance the accuracy of both detection and partition in the model.Whole-abdomen abdominal electronic CT scanning encompasses the entire abdominal region,extending from the lower edge of the sternum to the pelvic area.This comprehensive scan offers a detailed view of various organ structures within the abdomen.In clinical practice,special emphasis is often placed on the results of lower abdominal scans for the purpose of identifying lymph node metastasis,particularly in cases of colorectal cancer.However,the lower abdominal region represents only a small portion of the full abdominal scan.Furthermore,this area contains numerous organs,and the lymph nodes within it are typically small in size.Clinical detection of lymph nodes in this region is primarily performed manually by physicians,which poses significant challenges.Subsequent lymph node partition requires the determination of their relative positions,further adding to the complexity.To address the issue of lymph node detection,this paper introduces a lower abdominal frame discriminative model based on the assumption of a normal distribution.This model leverages spatial prior knowledge to specifically detect lymph nodes within the lower abdominal frame.For lymph node partition,the paper employs a specialized mask strategy,enhancing the model’s use of relative lymph node position information.It also incorporates a dual-attention mechanism module with direction-awareness and multi-scale information capture capabilities,ultimately achieving precise lymph node partition.This approach effectively utilizes spatial prior knowledge,improving the accuracy of lower abdominal frame discrimination while reducing redundant computations in subsequent lymph node detection models.The method further enhances the application of spatial invariance of bony structures within the lower abdominal region and the local details surrounding lymph node tissues through a customized mask design.The use of the dual attention mechanism module enhances the model’s ability to capture direction-aware information and effectively utilize global and multi-scale features.The paper presents the design and implementation of an intelligent system for abdominal lymph node detection and partition based on a browser/server architecture.This system has been deployed and tested in the Gastrointestinal Surgery Department of West China Hospital,Sichuan University,effectively reducing the clinical workload for physicians.3.Addressing the challenges posed by the complexity of organ tissues and the potential presence of noise and artifacts in medical images,this paper introduces an image segmentation model based on a U-shaped architecture with parallel convolutional attention mechanisms.By incorporating contour information to enforce shape constraints,this approach enhances the precision of medical image segmentation.Accurate medical image segmentation plays a pivotal role in extracting vital information from specific tissue images.It aids physicians in precisely locating anomalies and marking lesions,making it a crucial tool for disease diagnosis,treatment planning,and prognosis assessment.However,different types of medical images often exhibit inherent heterogeneity due to variances in imaging equipment and techniques.Some medical image categories feature significant variations in texture and shape,and they are susceptible to noise and artifact interference.Moreover,during the imaging process,factors such as patient respiration and movement can introduce deformations and distortions,adding to the challenge of segmenting target regions.In this paper,we introduce an approach that utilizes image contour infor-mation as prior shape knowledge for our model.We employ a U-shaped network architecture with skip connections to fuse multi-level features and incorporate a parallel convolutional attention mechanism to capture richer multi-scale features.This method extracts image contour information as shape priors and applies shape constraints during computation to facilitate the rapid localization of regions of interest.The U-shaped structure enhances the model’s contextual awareness,and the parallel convolutional attention mechanism efficiently processes multi-scale information while demonstrating robustness.Notably,it significantly enhances the performance of the segmentation model.Our proposed method achieved precise segmentation results in two different types of publicly available medical image datasets.It expedites the generation of diagnostic and treatment decisions and holds the potential to enhance the overall patient diagnostic and treatment experience.