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NiDAN Pathology Software

Whiteboard2Boardroom presents innovations from regional universities, hospitals, research organizations to encourage commercialization.

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NiDAN Pathology Software

  1. 1. Technology Snapshots April 30, 2019
  2. 2. What is the problem? • In 2017, FDA has approved a whole slide imaging system for primary diagnosis in digital pathology Presenter: Praveen Rao Source: Source: • However, whole slide images are very large in size • Over 1 billion pixels • 1 image can be 5-6 GB in size • Need 1 PB of storage for a million images • How do you store millions of WSIs and conduct fast analysis on them to automate disease diagnosis and improve pathologists’ workflow?
  3. 3. How does product/service solve problem? • NiDAN is a software built using open-source technologies – Low-cost storage – Scalable storage and fast retrieval of tiles using big data techniques – Image analytics support using deep learning • E.g., cervical cell classification using one- shot learning Presenter: Praveen Rao Value proposition: Pathologists in private practice will be 30% faster in their diagnosis using machine learning on whole slide images, thereby increasing their revenue by 20%.
  4. 4. What is the market use? • Digital pathology market size – $887M (by 2025)1 • Whole slide imaging market size Presenter: Praveen Rao 1
  5. 5. What competition exists? • Digital pathology companies – Leica Biosystems, Philips Healthcare, Ventana Medical Systems, Olympus, 3DHISTECH, and others • Tech companies like Google and Intel • Pharmaceutical and biotech companies Presenter: Praveen Rao
  6. 6. What is the status of the intellectual property? • Provisional patents have been filed – US Provisional Patent Application Serial No. 62/772,373, November 8, 2018 – US Provisional Patent Application Serial No. 62/688,104, June 21, 2018 Presenter: Praveen Rao
  7. 7. What is the stage of development? • We have developed a prototype of NiDAN – Source code on GitHub • Received NSF I-Corps grant – Conducted 100+ interviews • Two conference publications1,2 Presenter: Praveen Rao 2 Dig Vijay Kumar Yarlagadda, Praveen Rao, Deepthi Rao, and Ossama Tawfik. “A System for One-Shot Learning of Cervical Cancer Cell Classification in Histopathology Images.” In SPIE Medical Imaging: Digital Pathology Conference, 6 pages, San Diego, CA, 2019. 1 Daniel E. Lopez Barron, Dig Vijay Kumar Yarlagadda, Praveen Rao, Ossama Tawfik, and Deepthi Rao. “Scalable Storage of Whole Slide Images and Fast Retrieval of Tiles Using Apache Spark.” In SPIE Medical Imaging: Digital Pathology Conference, 6 pages, Houston, 2018.
  8. 8. What is needed for further development? • Funding is needed to accelerate software development and create a minimum viable product (MVP) • We are also looking for partnership with digital pathology companies Presenter: Praveen Rao
  9. 9. Our Team Presenter: Praveen Rao Praveen Ossama Daniel Deepthi Dig VijayMonica