CVPR 2023 - Label-Free Liver Tumor Segmentation

In this episode we discuss Label-Free Liver Tumor Segmentation by Qixin Hu, Yixiong Chen, Junfei Xiao, Shuwen Sun, Jieneng Chen, Alan Yuille, Zongwei Zhou. The paper discusses the use of synthetic tumors in CT scans to train AI models to accurately segment liver tumors without the need for manual annotation. These synthetic tumors are realistic in shape and texture and have proven effective in training the AI models, which demonstrated similar performance to models trained on real tumors. This highlights the potential for significantly reducing manual efforts for tumor annotation and the ability to improve the success rate of detecting small liver tumors, while also allowing for rigorous assessment of AI robustness.