EnvoyAI Partner:


logo-color.png
if_290_279051.png
Year Founded: 2013
if_search_244366.png

Area of Focus/Speciality:

  • Chest and breast imaging for radiology
  • Breast, colorectal, and lung cancer for digital pathology
if_hospital_clinic_133105.png

Hospital Associations:

  • Seoul National University Hospital, Seoul, Republic of Korea
  • Yonsei University Severance Hospital, Seoul, Republic of Korea
  • Samsung Medical Center, Seoul, Republic of Korea
  • Asan Medical Center, Seoul, Republic of Korea
if__location_1964213.png
Company Headquarters: Seoul, Republic of Korea
2538894-128.png
Number of Machines: 2

Lunit

About Lunit

Lunit, abbreviated from “learning unit,” is a venture company devoted to developing advanced medical image analytics and novel imaging biomarkers via cutting-edge deep learning technology. Lunit has proven high-end technology, recognized at international competitions such as ImageNet (5th place, 2015), TUPAC 2016 (1st place), and Camelyon 2017 (1st place), surpassing top companies like Google, IBM Research Zurich, and Microsoft Research Asia.

We envision a near-future when our systems would greatly help physicians make accurate, consistent, and efficient clinical decisions. We have so far focused on chest and breast imaging as well as digital pathology. Our algorithms for chest x-ray and mammography have been clinically validated to perform better than experts and significantly improve the diagnostic performance of its users.

At this year’s RSNA, Lunit is launching Lunit INSIGHT, a cloud-based real-time imaging AI platform that is currently available to the public at https://insight.lunit.io/. The platform delivers Lunit’s state-of-the-art AI models; the first one to be unveiled is the chest x-ray solution that accurately detects lung cancer, pneumothorax, tuberculosis, and pneumonia.

if_newspaper_193067.png

Press and Publications: 

  • RSNA 2017 Abstract Presentation: Automatic Detection of Malignant Pulmonary Nodules on Chest Radiographs Using a Deep Convolutional Neural Network: Detection Performance and Comparison with Human Experts [Chest (Lung Nodule) | Monday 10:30-10:40 AM | SSC03-01 | Room: S504CD]
  • RSNA 2017 Abstract Presentation: Deep Learning-based Automatic Detection Algorithm for the Detection of Malignant Pulmonary Nodules on Chest Radiographs [Chest (Lung Nodule) | Monday 10:50-11:00 AM | SSC03-03 | Room: S504CD]
  • RSNA 2017 Abstract Presentation: Advanced Data-Driven Imaging Biomarker for Breast Cancer Screening in Mammography [Science Session with Keynote: Breast Imaging (Deep Learning, Quantitative Imaging and Big Data) | Wednesday 11:00-11:10 AM | SSK02-04 | Room: E451A]
Lunit Leadership
Anthony Paek, Co-founder & Chief Executive Officer

Anthony Paek

Co-founder & Chief Executive Officer

Brandon B. Suh, Chief Medical Officer

Brandon B. Suh

Chief Medical Officer

Minhong Jang Co-founder & Chief Operating Officer

Minhong Jang

Co-founder & Chief Operating Officer

Jungin Lee, Co-founder & Chief Product Officer

Jungin Lee

Co-founder & Chief Product Officer

Lunit Machine | INSIGHT for Chest Radiography

Lunit is directly responsible for product and clinical representations

  • Lunit_INSIGHT_CXR_screenshot.jpg

Lunit INSIGHT for Chest Radiography accurately detects
lung nodules/mass (e.g. lung cancer), consolidation (e.g. pneumonia, tuberculosis), and pneumothorax in chest x-
ray images, presented via a heatmap and corresponding confidence score. Its accuracy levels reach 97-99% in AUC according to yet to be published clinical studies.

  • Training Data Source: Seoul National University Hospital in Republic of Korea
  • Number of Studies Trained On: +200k chest x-rays from CT/clinically-proven cases
  • Regulatory Status: Research use only

Lunit Machine | INSIGHT for Mammography

Lunit is directly responsible for product and clinical representations

  • Lunit_INSIGHT_MMG_screenshot.jpg

Lunit INSIGHT for Mammography accurately detects
breast cancer lesions in mammograms, presented via
a heatmap and corresponding confidence score. Its
accuracy levels reach +94% in AUC according to yet to
be published clinical studies.

  • Training Data Source: Yonsei University Severance Hospital, Asan Medical Center, Samsung Medical Center in Republic of Korea
  • Number of Studies Trained On: +200k mammograms (case), of which +50k are biopsy- proven malignant breast cancer cases
  • Regulatory Status: Research use only