no code implementations • 5 Jun 2023 • Yunlu Yan, Hong Wang, Yawen Huang, Nanjun He, Lei Zhu, Yuexiang Li, Yong Xu, Yefeng Zheng
To this end, we formulate this practical-yet-challenging cross-modal vertical federated learning task, in which shape data from multiple hospitals have different modalities with a small amount of multi-modality data collected from the same individuals.
1 code implementation • 2 Mar 2023 • Haozhe Liu, Wentian Zhang, Bing Li, Haoqian Wu, Nanjun He, Yawen Huang, Yuexiang Li, Bernard Ghanem, Yefeng Zheng
The evaluation results demonstrate that our AdaptiveMix can facilitate the training of GANs and effectively improve the image quality of generated samples.
no code implementations • 3 Jan 2023 • Yuexiang Li, Yawen Huang, Nanjun He, Kai Ma, Yefeng Zheng
The experimental results validate the superiority of our BOLT for medical image classification, compared to ImageNet pretrained weights and state-of-the-art self-supervised learning approaches.
1 code implementation • CVPR 2023 • Haozhe Liu, Wentian Zhang, Bing Li, Haoqian Wu, Nanjun He, Yawen Huang, Yuexiang Li, Bernard Ghanem, Yefeng Zheng
The evaluation results demonstrate that our AdaptiveMix can facilitate the training of GANs and effectively improve the image quality of generated samples.
1 code implementation • 5 Sep 2022 • Haoqin Ji, Haozhe Liu, Yuexiang Li, Jinheng Xie, Nanjun He, Yawen Huang, Dong Wei, Xinrong Chen, Linlin Shen, Yefeng Zheng
Such a point annotation setting can provide weakly instance-level information for abnormality localization with a marginal annotation cost.
1 code implementation • 6 Jun 2022 • Yao Zhang, Nanjun He, Jiawei Yang, Yuexiang Li, Dong Wei, Yawen Huang, Yang Zhang, Zhiqiang He, Yefeng Zheng
Concretely, we propose a novel multimodal Medical Transformer (mmFormer) for incomplete multimodal learning with three main components: the hybrid modality-specific encoders that bridge a convolutional encoder and an intra-modal Transformer for both local and global context modeling within each modality; an inter-modal Transformer to build and align the long-range correlations across modalities for modality-invariant features with global semantics corresponding to tumor region; a decoder that performs a progressive up-sampling and fusion with the modality-invariant features to generate robust segmentation.
Ranked #65 on Semantic Segmentation on NYU Depth v2
1 code implementation • 16 May 2022 • Haozhe Liu, Haoqin Ji, Yuexiang Li, Nanjun He, Haoqian Wu, Feng Liu, Linlin Shen, Yefeng Zheng
With the regularization and orthogonal classifier, a more compact embedding space can be obtained, which accordingly improves the model robustness against adversarial attacks.