7 Papers
Can Han is an academic researcher from Shanghai Jiao Tong University. The author has contributed to research in topics: Interpretability & Computer science. The author has co-authored 1 publications.
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Papers
Towards Open-set Gesture Recognition via Feature Activation Enhancement and Orthogonal Prototype Learning
Chen Liu,Can Han,Chengfeng Zhou,Crystal Xiaofang Cai,Suncheng Xiang,Hualiang Ni,Dahong Qian +6 more
TL;DR: This paper proposes a more effective PL method leveraging two novel and inherent distinctions, feature activation level and projection inconsistency, and introduces Orthogonal Prototype Learning (OPL) to construct multiple perspectives.
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MTDL-NET: Morphological and Temporal Discriminative Learning for Heartbeat Classification
Can Han,Suncheng Xiang,Dahong Qian +2 more
- 04 Jun 2023
TL;DR: Wang et al. as discussed by the authors proposed Masked Attention Embedding (MAE) for extracting discriminative morphological feature and Temporal feature enhanced mechanism for enhancing temporal representation of heartbeat.
1
MICCAI STSR 2025 Challenge: Semi-Supervised Teeth and Pulp Segmentation and CBCT-IOS Registration
Yaqi Wang,Zhi Li,Chengyu Wu,Jun Liu,Yifan Zhang,Jialuo Chen,Jiaxue Ni,Qian Luo,Jin Liu,Can Han,Changkai Ji,Zhi Qin Tan,Ajo Babu George,Liangyu Chen,Qianni Zhang,Dahong Qian,Shuai Wang,Huiyu Zhou +17 more
TL;DR: The MICCAI STSR 2025 Challenge benchmarked semi-supervised learning for digital dentistry tasks, including teeth and pulp segmentation and CBCT-IOS registration, with top teams achieving high Dice scores and Instance Affinity using deep learning-based methods.
SASG-DA: Sparse-Aware Semantic-Guided Diffusion Augmentation For Myoelectric Gesture Recognition
TL;DR: This paper proposes SASG-DA, a novel diffusion-based data augmentation approach for sEMG-based gesture recognition, leveraging semantic representations and sparse-aware sampling to generate faithful and diverse samples, significantly outperforming existing methods on benchmark datasets.
MICCAI STS 2024 Challenge: Semi-Supervised Instance-Level Tooth Segmentation in Panoramic X-ray and CBCT Images
Yaqi Wang,Zhi Li,Chengyu Wu,Jun Liu,Yifan Zhang,Jiaxue Ni,Qian Luo,Jialuo Chen,Hongyuan Zhang,Jin Liu,Can Han,Kaiwen Fu,Changkai Ji,Xi-Zhong Cai,Jing Hao,Zhihao Zheng,Shi Xu,Junqiang Chen,Qianni Zhang,Dahong Qian,Shuai Wang,Huiyu Zhou +21 more
TL;DR: The MICCAI STS 2024 Challenge benchmarked semi-supervised learning for instance-level tooth segmentation in panoramic X-ray and CBCT images, achieving significant performance gains over fully-supervised methods, with top models improving scores by 44-61 percentage points.