CVPR 2023 - Devil is in the Queries: Advancing Mask Transformers for Real-world Medical

In this episode we discuss Devil is in the Queries: Advancing Mask Transformers for Real-world Medical by Mingze Yuan, Yingda Xia, Hexin Dong, Zifan Chen, Jiawen Yao, Mingyan Qiu, Ke Yan, Xiaoli Yin, Yu Shi, Xin Chen, Zaiyi Liu, Bin Dong, Jingren Zhou, Le Lu, Ling Zhang, Li Zhang. The paper proposes a method for medical image segmentation that is capable of accurately identifying rare and clinically significant conditions, known as tail conditions. The method utilizes object queries in Mask Transformers to assign soft clusters during training and detect out-of-distribution (OOD) regions during inference, which is referred to as MaxQuery. The authors also introduce a query-distribution (QD) loss to improve segmentation of inliers and OOD indication. The proposed framework outperforms previous state-of-the-art algorithms on pancreatic and liver tumor segmentation tasks.