License Annotated Data

Leverage Caddie’s peer reviewed and published data across different diseases to fasten development

Caddie’s pre curated data to help data scientist, AI engineers save monumental time of staging data thereby fostering new insights in the field of radiological diagnosis.

Collaborators

Chosen through a rigorous selection process, our solution augments leading medical image data centers with unparalleled high-quality training and validation data

Multiple types of data annotations are available in the data portal, searchable in the data explorer as well as through custom Jupyter notebooks. Many more to come!

Available Annotations:

mRALE COVID severity scores

RICORD 1a

RICORD 1c

SIFT: Smart Imagery Framing and Truthing System

Smart Imagery Framing and Truthing (SIFT) System is an image annotation software system to assist annotators to identify abnormalities (or diseases) and their corresponding boundaries to train and test machine learning and deep learning artificial intelligence.

TCM Groups, Inc.

ImmunoQs.com

For the past several years, Caddie has collaborated with TB Portals of NIAID/NIH, the world most eminent AIDS and infectious diseases research institute, to develop, evaluate and apply SIFT to annotate over 10,000 TB radiographs (paper to be published in 2025 SPIE Medical Imaging Proceeding) in order to generate large amount of high-quality data available in TB Portals for researchers worldwide to train AI model for screening TB as well as for other clinical practices which is in line with the mission of NIH “enhance health, lengthen life, and reduce illness and disability”.

What's Clients Says?

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ChexPert from Stanford U

263,621 13 different diseases

Chest X-ray14 from Clinical Center of NIH

141,537 12 different diseases

MIDRC/NIBIB/NIH from 30+ hospitals

160,020 COVD-1

TB Portals NIAID/NIH

14,531 TB Cases

Cytopathology

e.g., cancer, disease : 33,100

Histopathology

e.g., tissue : 12,300

ChestXpert SIFT Accuracy

Our SIFT tool offers superior accuracy in detecting various diseases.

Accuracy: >90% accuracy see above
Time: multiple solution exists for annotated Data ingestion for training AI models
Cost: 50-75% cost effective
Large amount: >260k available for training and testing

Stanford University Hospital has released over 260,000 lesions including 13 different lesions see product specification appearing on 116, 21 DICOM chest x-ray.
Original data location https://stanfordmlgroup.github.io/competitions/chexpert/

Industries We Serve

Healthcare

Auto

Manufacturing

Technology

Retail

Defence

Diverse Annotation Experience

Extensive Experience Across Various Imaging Modalities. Our team has a wealth of experience in annotating a wide range of images.

Digital x-ray images: 57 ,706 studies

ChexPert from Stanford U: 263,621 13 different diseases
Chest X-ray14 from Clinical Center of NIH: 141,537 12 different diseases
MIDRC/NIBIB/NIH from 30+ hospitals: 160,020 COVD-1
TB Portals NIAID/NIH: 14,531 TB Cases

Pathological Images: 46,051 studies

Cytopathology e.g., cancer, disease : 33,100
Histopathology e.g., tissue : 12,300

Endoscopic imaging video for lung: – this is used for diagnosis purpose. For the past three years, they already have 6,000 video.

Video-assisted thoracic surgery (VATS) for lung cancer treatment – they already have over 30,000 videos.

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Ste 300 #1238
Gaithersburg, MD 20879

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