Synthetic Tumors Make AI Segment Tumors Better

-д хадгалсан:

Номзүйн дэлгэрэнгүй
-д хэвлэсэн:	arXiv.org (Oct 26, 2022), p. n/a
Үндсэн зохиолч:	Hu, Qixin
Бусад зохиолчид:	Xiao, Junfei, Chen, Yixiong, Sun, Shuwen, Chen, Jie-Neng, Yuille, Alan, Zhou, Zongwei
Хэвлэсэн:	Cornell University Library, arXiv.org
Нөхцлүүд:	Tumors Annotations Training
Онлайн хандалт:	Citation/Abstract Full text outside of ProQuest
Шошгууд:	Шошго нэмэх Шошго байхгүй, Энэхүү баримтыг шошголох эхний хүн болох!

Тодорхойлолт
Хураангуй:	We develop a novel strategy to generate synthetic tumors. Unlike existing works, the tumors generated by our strategy have two intriguing advantages: (1) realistic in shape and texture, which even medical professionals can confuse with real tumors; (2) effective for AI model training, which can perform liver tumor segmentation similarly to a model trained on real tumors - this result is unprecedented because no existing work, using synthetic tumors only, has thus far reached a similar or even close performance to the model trained on real tumors. This result also implies that manual efforts for developing per-voxel annotation of tumors (which took years to create) can be considerably reduced for training AI models in the future. Moreover, our synthetic tumors have the potential to improve the success rate of small tumor detection by automatically generating enormous examples of small (or tiny) synthetic tumors.
ISSN:	2331-8422
Эх сурвалж:	Engineering Database