What's new in 2022?
🎁️ Prizes and 🛡️ e-Badges for Winners
Placement | 1st Place | 2nd Place | 3rd Place | Audience Choice |
---|---|---|---|---|
Award Amount | $1500 | $1000 | $500 | $500 |
These amounts are redeemable for SIIM educational initiatives such as SIIMU (self-paced online learning) and SIIM Training (instructor-led training).
Winners will also get certified e-badges that you can proudly display on your social-media profiles (e.g. LinkedIn).
📖️ New Documentation
You are looking at it right now! Hope you find it spiffy 😉️
⌨️ Interactive Coding Tutorials
These can help you learn FHIR and/or DICOMweb a little faster, and provide code samples you can build upon!
- FHIR: https://replit.com/@mohannadhussain/fhir-example
- DICOMweb: https://replit.com/@mohannadhussain/dicom-web-example
⛓️ Private Ethereum Network (Blockchain implementation)
API available at http://hackathon.siim.org/ethereum/
NOTE http NOT https
See 5-Minute Ethereum Starting Guide for more information.
☢️ DICOM image samples from Dermatology, Ophthalmology and Pathology
The easiest way to find each is to search DICOMweb by Modality, see more detailed examples below.
Dermatology
https://hackathon.siim.org/dicom-web/studies?ModalitiesInStudy=XC
XC = External Capture, which is NOT always dermatology, but on our server all XCs are Dermatology images)
Thanks to ISIC for providing these samples from the 2020 SIIM-ISIC Challenge
Ophthalmology
https://hackathon.siim.org/dicom-web/studies?ModalitiesInStudy=OP
OP = Ophthalmic Photography
Thanks to Dr. Damien Luviano and Michael Turano for providing these samples.
Pathology
https://hackathon.siim.org/dicom-web/studies?ModalitiesInStudy=SM
SM = Slide Microscopy
Thanks to DICOM WG-26 and Dr. Markus D. Herrmann
💡️ Machine Learning Tutorials
Sample code to get you up and running with your Machine Learning journey, in the form of Jupyter Notebooks. Thanks to Dr. Eduardo Farina and his team at Dados e Saúde for providing those.
- DICOM Window-levelling for Brain CT Scans. Read more about windowing explained.
- Neural Network training for Tuberculosis Detection (work in progress).