1 code implementation • 11 Jul 2023 • Linhao Qu, Yingfan Ma, Zhiwei Yang, Manning Wang, Zhijian Song
In this paper, we formulate this scenario as an open-set AL problem and propose an efficient framework, OpenAL, to address the challenge of querying samples from an unlabeled pool with both target class and non-target class samples.
no code implementations • 5 Jul 2023 • Linhao Qu, Yingfan Ma, Xiaoyuan Luo, Manning Wang, Zhijian Song
In this paper, we propose an instance-level MIL framework based on contrastive learning and prototype learning to effectively accomplish both instance classification and bag classification tasks.
no code implementations • ICCV 2023 • Linhao Qu, Zhiwei Yang, Minghong Duan, Yingfan Ma, Shuo Wang, Manning Wang, Zhijian Song
However, there are still three important issues that have not been fully addressed: (1) positive bags with a low positive instance ratio are prone to the influence of a large number of negative instances; (2) the correlation between local and global features of pathology images has not been fully modeled; and (3) there is a lack of effective information interaction between different magnifications.