Cell Detection by Robust Self-Trained Networks

Yuang Zhu, Yuxin Zheng, Zhao Chen
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Abstract

Cell nuclear detection on digital histopathology images plays an important role on computer-assisted cancer diagnostics. However, lack of manual annotations and variability of cells bring great challenges to fully-supervised learning. Therefore, we propose a Robust Self-Trained Network (RSTN) for cell detection. The backbone is an encoder-decoder trained by distance maps (DMs) generated from dot annotations of nuclei. To save manual efforts, RSTN is designed to involve reliable predicted DMs in optimization and detect cell centers for unknown images automatically. RSTN gains robustness by regularizing the network by dynamic graphs of DM patches. It exploits underlying graph structures and recognizes complex spatial patterns to locate cells of various shapes and colors. Experimental results show that it outperforms several classic and advanced models on both simulated fluorescence microscope images and real pathology slides for cell detection.
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基于鲁棒自训练网络的细胞检测
数字组织病理学图像上的细胞核检测在计算机辅助癌症诊断中起着重要作用。然而,缺乏人工注释和细胞的可变性给全监督学习带来了很大的挑战。因此,我们提出了一种鲁棒自训练网络(RSTN)用于细胞检测。主干是由核的点注释生成的距离图(dm)训练的编解码器。为了节省人工工作,RSTN在优化中引入了可靠的预测dm,并自动检测未知图像的细胞中心。RSTN通过DM补丁的动态图对网络进行正则化,从而获得鲁棒性。它利用底层的图形结构和识别复杂的空间模式来定位各种形状和颜色的细胞。实验结果表明,该方法在模拟荧光显微镜图像和真实病理切片细胞检测上都优于几种经典和先进的模型。
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