面向医疗 AI 的医学多模态数据平台

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Collage showing medical scenes including a surgical monitor displaying an operation, blue prescription pills, an X-ray of a broken forearm with highlighted fracture, a man wearing a mask using a smartphone, and a microscopic view of cells.

面向医疗 AI 的医学数据整理与标注

Enhance healthcare AI with precise data annotation, enabling accurate medical image analysis, diagnostics, and improved patient care solutions.
X-ray image of a forearm showing a bone fracture near the wrist with a green square highlighting the injury.

放射影像标注

X-Ray annotation involves labeling critical areas within X-ray images, such as bones, organs, or abnormalities, to train AI models for accurate diagnostics and medical image analysis. This process supports automated detection of fractures, lung diseases, and other health conditions, enhancing clinical decision-making.
Chest X-ray image showing lungs with two highlighted green areas near the upper lobes.

X-Ray Cancer Detection

X-Ray cancer detection involves annotating medical images to identify signs of cancer, such as tumors or abnormal tissue patterns. This process enables AI models to assist healthcare professionals in early diagnosis, improving treatment planning and patient outcomes through accurate and timely analysis.
Two medical professionals in masks and gloves examining a colonoscopy image with highlighted areas on a monitor.

手术视频标注

Surgical assistance leverages AI and data annotation to enhance precision during procedures. By analyzing medical images and tracking surgical tools in real time, it supports surgeons with accurate guidance, improves decision-making, and reduces the risk of complications, leading to better patient outcomes.
Microscopic view of blood cells with several dark purple cells outlined in green boxes indicating detected cancerous or abnormal cells.

Cancer Screening

Cancer screening involves using advanced AI models and annotated medical data to detect early signs of cancer. By analyzing imaging data such as X-rays, CT scans, and MRIs, this technology supports healthcare professionals in identifying abnormalities, enabling timely diagnosis and improved treatment outcomes.
Panoramic dental X-ray showing teeth with braces and four highlighted impacted wisdom teeth in green boxes.

Teeth X-Ray Issue Detection

Teeth X-ray issue detection involves analyzing annotated dental X-ray images to identify problems such as cavities, fractures, impacted teeth, and gum diseases. This advanced technique supports dentists in accurate diagnostics, treatment planning, and improving overall dental care outcomes.
MRI brain scan showing a highlighted tumor area in the left frontal lobe.

Brain Tumors Screening

Brain tumor screening involves the analysis of annotated brain imaging data, such as MRIs and CT scans, to detect and classify tumors. This process aids healthcare professionals in early diagnosis, precise treatment planning, and monitoring disease progression, enhancing patient care and outcomes.
A hand holding eight pills, including three white oblong pills with a score line and three green triangular pills marked with '20'.

Pill Recognition

Pill recognition involves using AI and image annotation to identify and classify pharmaceutical tablets based on shape, size, and markings. This technology supports medication management, improving accuracy in prescriptions, drug interactions, and patient safety.
A woman in the foreground and two men in the background sitting side by side on a bench, all wearing green protective face masks.

PPE Monitoring

PPE monitoring involves the use of AI and real-time image analysis to ensure healthcare workers are wearing the appropriate personal protective equipment (PPE). This technology helps maintain safety standards, reduce infection risks, and enhance workplace safety in high-risk environments.

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医疗领域的标注类型

Healthcare annotation involves labeling medical data, such as images and records, to train AI models for diagnostics, disease detection, and treatment planning, enhancing healthcare solutions.
Pile of white and light green oblong tablets scattered on a blue surface.

Bounding Box for Healthcare

Bounding box annotation for healthcare involves marking rectangular regions around areas of interest in medical images, such as tumors, organs, or anomalies. This technique is essential for training AI models in diagnostics, medical imaging analysis, and automated healthcare solutions.

Segmentation for Healthcare

Segmentation for object classification in healthcare involves precisely outlining and labeling regions in medical images, such as organs, tissues, or abnormalities. This advanced technique enables AI models to perform accurate diagnostics, enhance image analysis, and support personalized treatment planning.

Axial brain MRI scan showing a highlighted green region in the lower right area of the brain.

医疗 AI 常见问题

Unitlab AI 可以标注哪些医疗数据？

Unitlab 支持 DICOM、CT、MRI、NIfTI、NRRD、全切片病理、临床文档、文本、图像、视频及关联多模态记录。

Unitlab 如何支持 DICOM 和三维医学影像？

医疗团队可使用同步多平面视图、体积分割、临床属性、时间线和检查级审查处理 CT 与 MRI 数据。

Unitlab 支持全切片病理标注吗？

支持。团队可通过深度缩放、组织区域、细胞与细胞核实例、分割、发现和病理本体审查全切片图像。

临床文档和文本可以与影像一起标注吗？

可以。文档区域、OCR 字段、文本实体、关系、分类和医学影像可在关联多模态流程中统一管理。

临床本体和属性如何管理？

团队可定义受控类别、发现、属性和关系，使不同检查、专家与数据集版本之间的标注保持一致。

Unitlab 提供 AI 辅助医疗标注吗？

AI 辅助分割和模型闭环流程可以加速标注，同时保留专家审查和修正控制。

医学标注质量保证如何运作？

审查角色、问题、返工、审批步骤、标注历史和数据集版本支持可追溯的专家审查。

敏感医疗数据可以保留在本地吗？

可以。本地部署适用于需要将医疗数据、模型和流程保留在受控基础设施中的组织。

Unitlab 能管理大型检查和纵向序列吗？

Unitlab 支持大型医疗文件、关联序列、检查上下文、协作审查和版本化数据集。

相关资源：医学标注病理标注文档标注标注质量保证标注文档
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