Research on Light Field Correction Based on LinkNet in HER2 Pathological Image

Abstract

The assessment of pathological Images plays a crucial role in cancer cure and research. The automatic evaluation method based on artificial intelligence can provide assistance for doctors and effectively improve the efficiency and accuracy of doctors’ diagnosis. However, the current methods based on artificial intelligence have the problem of low quality of collected pathological images, which has a negative impact on the prediction of intelligent algorithms. Due to the influence of the illumination deviation, the brightness distribution of the collected pathological images is uneven, and the noise generated by the uneven illumination will be mixed with the useful signal in the image, and some details are partially blurred or completely obscured. To tackle these problems, this paper proposes a LinkNet-based light field correction method for polarization correction of pathological images. This method takes advantage of Gaussian image characteristics to generate a large number of images with different center points and different brightness diffusion speeds to simulate the light field distribution map, and uses WSI (Whole-slides Image) as a standard unbiased pathological image to train a polarization correction model. Compared with traditional image enhancement methods, the method proposed in this paper is not only the best in terms of visual effect contrast, but also has very desirable effects in image light field correction and detail enhancement. The experimental results show that the method proposed in this article and other evaluation methods we use are the best in optional various index evaluations.