Medical Data Annotation

Medical Data Annotation是MedicalAI Model Training的基础Link,PassProfessional annotation transforms raw medical data into structured information。其核心在于将Imaging(CT/MRI)、Text(Medical RecordRecord)、信No.(ECG)等Data添加MedicalLabel,如OncologySegmentation、DiseaseClassification等,要求Annotation人员具备MedicalKnowledge with rigorous standards(DICOM、ICD-10)。This process directly impactsAIDiagnosis的Accuracy,Must pass multi-tier quality inspection and expert review to ensure data quality,同hr需符合HIPAA等隐私Regulation。

Medical Data Annotation现状Analysis

Data孤岛

HIS、PACS等重要Data难以互通共享。

difficult to realize value

Data价值Achieve面临重重困境。

合规风险

DataDue to its high sensitivity,Prone to compliance violations during sharing and trading。

3D Data Complexity

Medical Imaging AnnotationMulti-planar processing requiredReconstruction(MPR)Data,Traditional tools produce layer stacking errors in 3D slice annotation。

MedicalDataCollaborationEcosystem圈

DataGroup
Datatier/交易场/Hospital紧密Collaboration,共同推动MedicalData Mart场繁荣,AchieveMedicalData价值最大化,助力Medical产业High-quality发展。
Hospital
构建High-qualityMedical Data Project Cooperation,Physician参与Data Collection、清洗、Annotation等,推进辅助Diagnosis & Treatment大Model的Development。
药企与AIEnterprise
CustomizedData Service(如采集、清洗、Annotation等)满足性化需求,共促MedicalData高效应用,携手推动MedicalDomain创新发展。

How we solve these challenges for you

01

Data Collection & De-identification

MedicalData Source包括电子Medical Record、MedicalImaging(CT/MRI/X光)、Clinical研究Data等,需Coverage不同yr龄segment、Disease阶segment及Medical InstitutionType。De-identification required after collection,移除Patient姓名、身recordsCertificate numbers and direct identifiers,And sensitive information such as rare diseasesVirtualizedProcessing。

Data Collection & De-identification
Data Preprocessing & Standardization

02

Data Preprocessing & Standardization

Raw Data需进row清洗(去除Duplicate内容、纠正Medical Record错别字、剔除模糊Imaging)和Format转换(统一Imagingmin辨率、SpecMedical TerminologyTable述)。E.g., harmonizing data from different vendorsDICOMImagingNormalized to uniform pixel depth,TextData采用ICD-11International Classification of DiseasesStandardEncoding。

03

Annotation Guideline Development

由Medical Experts制定Annotation Guide,明确EntityRecognition Standard(如DiseaseNameAnnotation需包含全称、别名及ICDEncoding)。Annotation Team需具备Medical背景,PassCase Study演练掌握BIOAnnotation法(Entity起始/internal/外部标记)And professional tool usage。

Annotation Guideline Development
Multi-Modal Annotation Execution

04

Multi-Modal Annotation Execution

Text Annotation:采用NER技术AnnotationMedical Record中的症状、DrugEntity,并建立症状-DiseaseRelated关系。
ImagingAnnotation:Polygon tool annotationOncology边界,Annotation病变Type及minlevel(如Pulmonary NoduleSize/密度)。
骨骼点Annotation:LocalizationJoint关键点,用于RehabilitationTrainingSolution制定。

05

Quality control and auditing

采用3-Tier QA机制:Annotation员自检(Accuracy Rate≥95%)、质检组抽样Examination(Recall Rate≥90%)、Medical Expert Final Review。Disputed cases require multidisciplinary consultation to determine annotation results。

Quality control and auditing
DataDelivery与ModelTraining

06

DataDelivery与ModelTraining

OutputStructured DataSpec(如CSV/JSONFile)及配套Document,包含Data SourceExplanation、Annotation规则Version等。Delivery后需进rowModelVerify,如ImagingRecognitionModel需PassROC曲线AssessmentDiagnosis效能。

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