ANT-UNet: Accurate and Noise-Tolerant Segmentation for Pathology Image Processing
Yufei Chen, Qinming Zhang, Tingtao Li, Hao Yu, Mei Tian, Cheng Zhuo · 2019
Pathology image segmentation is an essential step in early detection and diagnosis for various diseases. Due to its complex nature, precise segmentation is not a trivial task. Recently, deep learning has been proved as an effective option for pathology image processing. However, its efficiency is highly restricted by the inconsistent annotation quality. In this paper, we propose an accurate and noise-tolerant segmentation approach to overcome the aforementioned issues, which consists of a pre-processing module for data augmentation, a new neural network architecture, ANT-UNet, and a FCCRF inference module. Experimental results demonstrate that, even on a noisy dataset, the proposed approach can achieve more accurate segmentation with 4-23% accuracy improvement than other commonly used segmentation methods. Moreover, the proposed architecture is hardware-friendly and can be incorporated with a GPU acceleration flow to reach 24-128× speed-up.