|2022-7-17||Release of training data – 1|
|2022-9-17||Release of training data – 2|
|2022-10-17||Release of training data – 3|
|2022-11-17||Release of validation images|
|2022-12-1||Release of training data – 4|
|2022-12-14||Release of test images|
|2022-12-17 9:40~11:00AM CST||Presentations at workshop|
|Evergreen International Convention Center|
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You will get the token for submission one or two days after sign up.
The task is segmentation. The participants are expected to develop their method and produce segmentation labels of the brain tumors to treat, by using the training data.
According to the points of each team in the final evaluation, we select the highest three teams for regular awards.
The prize will only be awarded to teams giving in-person presentation at workshop (Taipei, 2022-12-17).
- Champion: AI training server (4U rackmount) x 1
- 1st Runner-up: 16TB HDD x 1
- 3rd-place: 2TB SSD x 1
For some reasons, we were prohibited from providing real patient dataset in an open way. Alternatively, we are releasing open datasets generated by DDPM1. Our initial test showed that models trained using synthetic images performed just as well as using real images, if not better. Because synthetic data may make AI systems better and even more ethical2, ICTS 2022 will adopt our open synthetic datasets.
If you are still interested in the real patient dataset, NTUH requested a formal contract. Please contact us for further information.
- 1. Dorjsembe, Z., Odonchimed, S., & Xiao, F. (2022, April). Three-Dimensional Medical Image Synthesis with Denoising Diffusion Probabilistic Models. In Medical Imaging with Deep Learning.
- 2. Strickland, E. (2022, March 10). Are you still using real data to train your AI? IEEE Spectrum. Retrieved July 10, 2022, from https://spectrum.ieee.org/synthetic-data-ai