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Medical Annotation in Pathology

Use AI to Understand Complex Patterns and Anomalies in Tissue Slides

Data Annotation Use Cases in Digital Pathology

Superior labeling for developing AI driven computational pathology (CPATH)

Semantic Segmentation for Tissue Differentiation

Use Case: Essential for detailed histological analysis, semantic segmentation aids in differentiating between cancerous and non-cancerous tissues, significantly bolstering the accuracy of biopsy evaluations. This is crucial in automated whole-slide imaging. Technique: This involves pixel-wise annotations that meticulously outline and categorize different tissue types within a slide.
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Bounding Box Annotation for Localization

Use Case: Used primarily to identify and quantify particular cells of interest, such as tumor cells or infectious agents (immunohistochemical biomarkers such as Ki67, PD-L1), facilitating focused analyses and treatment planning. Technique: Applying simple rectangular annotations to precisely localize specific cellular formations or anomalies within a tissue slide.
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Keypoint Annotation for Structural Markings in Cells

Use Case: Key in the study of cellular behaviours and pathology development, such as tracking the progression of metastatic cancer cells or other pathologies that alter cellular structures. Also essential in developing AI tools that standardize scoring criteria in several tumors, such as Gleason score. Technique: Pinpointing critical structural landmarks within cells or tissue architectures which are vital for morphological studies.
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Polygon Annotation for Irregularly Shaped Cells

Use Case: Incredibly useful in complex cases where pathologies do not conform to standard shapes, such as in certain types of melanoma or irregular fibrosis patterns. It is also the basis in the development of AI search tools such as content-based image retrieval (CBIR) specially in Digital Pathology. Technique: Drawing detailed polygons around irregularly shaped pathological features to capture the precise dimensions and shape.
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Building AI in Digital Pathology Dentistry

Labeling guided by doctors and experts in the field of Pathology to ensure that training data re medically relevant for AI in Pathology.

In the past, we have tailored our use cases to build training data for content-based image retrieval (CBIR), AI categorisation of Gleason score and digital image analysis (DIA) platforms. Our experts will work with you to custom our annotations for your AI tech.

Scale the labeling of training data and increase the overall data quality with us and we assure you that the precision of your pathological ML models will improve.

Complexity of Pathology AI Simplified With Annotation Labs

Pathological Data Annotations supervised by Licensed Doctors for unmatched Accuracy

Why Choose Annotation Labs?

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Supervised by Doctors

Our workforce is assisted and overlooked by medical professionals and doctors ensuring 100% accurate annotations

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100% Privacy Compliant

Stringent regulatory and security standards necessary with adherence to HIPAA & GDPR regulations.

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Dedicated Project Manager

Each client benefits from a dedicated manager, ensuring personalized oversight and alignment of progress with client goals

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Enterprise Grade Solution

We deliver high-quality, scalable data solutions that meet the stringent demands of global-level AI & ML projects

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If SLAs are Not Met, Its FREE
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200+ Successful Projects

4.8/5 CSAT score

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