Diversity-Aware Label Distribution Learning for Microscopy Auto Focusing
Chuyan Zhang, Yun Gu, Jie Yang, Guang‐Zhong Yang
Abstract
Optical microscopy imaging is the gold standard for the diagnosis of cancers since it allows the cell-level visualization of tissues. The high quality of imaging is largely determined by the focus distances between the lens and objects. Therefore, a robust and efficient auto focusing algorithm is required to obtain the optimal focus position, especially for the robot-assisted microscopy systems. In this letter, we propose a diversity-aware learning framework to predict the optimal focus position based on a single image, without any reference. For robust and accurate estimation, the two-point representation of distance to the optimal focus position is utilized for label distribution learning. To reduce the intra-class variation caused by the diversity of pathological slides, we present a intraclass discrepancy penalty term following the composite-loss and the gradient-domain input strategy to concentrate more on image focus quality. Experiments on real microscopy datasets demonstrate that the proposed method achieves the promising performance in terms of accuracy, real-time and generalization. The mean absolute error is 0.308 μm, which is within the depth-of-field of the microscope. It outperforms the previous no-reference approaches by 39%.