Medical Data Annotation
Current State of Medical Data Annotation
Data Silos
Critical data from HIS and PACS systems remains difficult to share and interoperate.
difficult to realize value
Unlocking data value faces numerous challenges.
Compliance Risks
Due to the high sensitivity of medical data, sharing and trading are prone to compliance violations.
3D Data Complexity
Medical imaging annotation requires multi-planar reconstruction (MPR) processing. Traditional tools produce layer-stacking errors in 3D slice annotation.
Medical Data Collaboration Ecosystem
How we solve these challenges for you
01
Data Collection & De-identification
Medical data sources include electronic medical records, medical imaging (CT/MRI/X-ray), and clinical research data, covering different age groups, disease stages, and types of medical institutions. De-identification is required after collection to remove patient names, ID numbers, and other direct identifiers, with virtualized processing applied to sensitive information such as rare diseases.
02
Data Preprocessing & Standardization
Raw data requires cleaning (removing duplicates, correcting medical record typos, filtering blurry images) and format conversion (standardizing image resolution and medical terminology). For example, harmonizing DICOM images from different vendors to uniform pixel depth, and encoding text data using the ICD-11 International Classification of Diseases standard.
03
Annotation Guideline Development
Medical experts develop annotation guidelines that define entity recognition standards (e.g., disease name annotations must include full names, aliases, and ICD codes). Annotation teams must have a medical background and master BIO annotation methods (entity beginning/inside/outside tagging) and professional tool usage through case study exercises.
04
Multi-Modal Annotation Execution
Text Annotation: Using NER technology to annotate symptoms and drug entities in medical records, and establishing symptom-disease relationships.
Imaging Annotation: Using polygon tools to annotate tumor boundaries, lesion types, and detail levels (e.g., pulmonary nodule size/density).
Skeleton Point Annotation: Localizing joint key points for rehabilitation training solution development.
05
Quality control and auditing
Adopting a 3-tier QA mechanism: annotator self-check (accuracy rate >= 95%), quality inspection team sampling review (recall rate >= 90%), and medical expert final review. Disputed cases require multidisciplinary consultation to determine annotation results.
06
Data Delivery & Model Training
Outputting structured data specifications (such as CSV/JSON files) and supporting documentation, including data source descriptions and annotation guideline versions. After delivery, model verification is required, such as using ROC curve assessment to evaluate the diagnostic performance of imaging recognition models.