no code implementations • 20 Dec 2023 • Yuan Zhang, Yaolei Qi, Xiaoming Qi, Lotfi Senhadji, Yongyue Wei, Feng Chen, Guanyu Yang
Federated learning (FL) for histopathology image segmentation involving multiple medical sites plays a crucial role in advancing the field of accurate disease diagnosis and treatment.
2 code implementations • ICCV 2023 • Yaolei Qi, Yuting He, Xiaoming Qi, Yuan Zhang, Guanyu Yang
In this work, we note the specificity of tubular structures and use this knowledge to guide our DSCNet to simultaneously enhance perception in three stages: feature extraction, feature fusion, and loss constraint.
no code implementations • MICCAI 2022 2022 • Xiaoming Qi, Guanyu Yang, Yuting He, Wangyan Liu, Ali Islam, Shuo Li
In this work, a cross-center cross-sequence medical image segmentation FL framework (FedCRLD) is proposed for the first time to facilitate multi-center multi-sequence CMR segmentation.
2 code implementations • 10 May 2022 • Zhangfu Dong, Yuting He, Xiaoming Qi, Yang Chen, Huazhong Shu, Jean-Louis Coatrieux, Guanyu Yang, Shuo Li
The nature of thick-slice scanning causes severe inter-slice discontinuities of 3D medical images, and the vanilla 2D/3D convolutional neural networks (CNNs) fail to represent sparse inter-slice information and dense intra-slice information in a balanced way, leading to severe underfitting to inter-slice features (for vanilla 2D CNNs) and overfitting to noise from long-range slices (for vanilla 3D CNNs).
no code implementations • 8 Jun 2021 • Yuting He, Rongjun Ge, Xiaoming Qi, Guanyu Yang, Yang Chen, Youyong Kong, Huazhong Shu, Jean-Louis Coatrieux, Shuo Li
3)We propose the adversarial weighted ensemble module which uses the trained discriminators to evaluate the quality of segmented structures, and normalizes these evaluation scores for the ensemble weights directed at the input image, thus enhancing the ensemble results.
3 code implementations • 5 Jan 2019 • Enlu Lin, Qiong Chen, Xiaoming Qi
The agent finally finds an optimal classification policy in imbalanced data under the guidance of specific reward function and beneficial learning environment.