1 code implementation • 18 May 2024 • Ximiao Zhang, Min Xu, Dehui Qiu, Ruixin Yan, Ning Lang, Xiuzhuang Zhou
To address this, we design a series of medical image anomaly synthesis tasks to simulate common disease patterns in medical imaging, transferring the powerful generalization capabilities of CLIP to the task of medical image anomaly detection.
no code implementations • 18 Mar 2024 • Mengwei Wang, Ruixin Yan, Zeyi Hou, Ning Lang, Xiuzhuang Zhou
On one hand, the construction of a Chinese chest X-ray report dataset is limited by the time-consuming and costly process of accurate expert disease annotation.
1 code implementation • 9 Mar 2024 • Ximiao Zhang, Min Xu, Xiuzhuang Zhou
Self-supervised feature reconstruction methods have shown promising advances in industrial image anomaly detection and localization.
no code implementations • 26 Dec 2023 • Zeqiang Wei, Kai Jin, Xiuzhuang Zhou
However, due to task competition and information interference caused by significant differences between the inputs of the two proxy tasks, the effectiveness of representation learning for intra-modal and cross-modal features is limited.
1 code implementation • 16 Nov 2023 • Haoqi Ni, Ximiao Zhang, Min Xu, Ning Lang, Xiuzhuang Zhou
Chest X-Ray (CXR) examination is a common method for assessing thoracic diseases in clinical applications.
Supervised Anomaly Detection Weakly-supervised Anomaly Detection
2 code implementations • 25 Oct 2022 • Min Xu, Ximiao Zhang, Xiuzhuang Zhou
In this paper, we investigate the problem of prediction confidence in face and kinship verification.
no code implementations • TPAMI 2013 • Jiwen Lu, Xiuzhuang Zhou, Yap-Pen Tan, Yuanyuan Shang, Jie zhou
In this paper, we propose a new neighborhood repulsed metric learning (NRML) method for kinship verification.
Ranked #5 on Kinship Verification on KinFaceW-I