Analyzing Tumors by Synthesis

no code implementations • 9 Sep 2024 • Qi Chen, Yuxiang Lai, Xiaoxi Chen, Qixin Hu, Alan Yuille, Zongwei Zhou

We also present case studies in the liver, pancreas, and kidneys reveal that AI trained on synthetic tumors can achieve performance comparable to, or better than, AI only trained on real data.

Synthetic Data as Validation

no code implementations • 24 Oct 2023 • Qixin Hu, Alan Yuille, Zongwei Zhou

Specifically, the DSC score for liver tumor segmentation improves from 26. 7% (95% CI: 22. 6%-30. 9%) to 34. 5% (30. 8%-38. 2%) when evaluated on an in-domain dataset and from 31. 1% (26. 0%-36. 2%) to 35. 4% (32. 1%-38. 7%) on an out-domain dataset.

Computed Tomography (CT) Continual Learning +1

Animal3D: A Comprehensive Dataset of 3D Animal Pose and Shape

no code implementations • ICCV 2023 • Jiacong Xu, Yi Zhang, Jiawei Peng, Wufei Ma, Artur Jesslen, Pengliang Ji, Qixin Hu, Jiehua Zhang, Qihao Liu, Jiahao Wang, Wei Ji, Chen Wang, Xiaoding Yuan, Prakhar Kaushik, Guofeng Zhang, Jie Liu, Yushan Xie, Yawen Cui, Alan Yuille, Adam Kortylewski

Animal3D consists of 3379 images collected from 40 mammal species, high-quality annotations of 26 keypoints, and importantly the pose and shape parameters of the SMAL model.

Animal Pose Estimation

Label-Free Liver Tumor Segmentation

1 code implementation • CVPR 2023 • Qixin Hu, Yixiong Chen, Junfei Xiao, Shuwen Sun, Jieneng Chen, Alan Yuille, Zongwei Zhou

We demonstrate that AI models can accurately segment liver tumors without the need for manual annotation by using synthetic tumors in CT scans.

Segmentation Tumor Segmentation

Synthetic Tumors Make AI Segment Tumors Better

1 code implementation • 26 Oct 2022 • Qixin Hu, Junfei Xiao, Yixiong Chen, Shuwen Sun, Jie-Neng Chen, Alan Yuille, Zongwei Zhou

We develop a novel strategy to generate synthetic tumors.

Tumor Segmentation