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首页> 外文会议>European congress on digital pathology >A Fast Pyramidal Bayesian Model for Mitosis Detection in Whole-Slide Images
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A Fast Pyramidal Bayesian Model for Mitosis Detection in Whole-Slide Images

机译：快速金字塔形全贝叶斯模型在全幻灯片图像中的有丝分裂检测。

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摘要

Mitosis detection in Hematoxylin and Eosin images and its quantification for mm2 is currently one of the most valuable prognostic indicators for some types of cancer and specifically for the breast cancer. In whole-slide images the main goal is to detect its presence on the full image. This paper makes several contributions to the mitosis detection task in whole-slide in order to improve the current state of the art and efficiency. A new coarse to fine pyramidal model to detect mitosis is proposed. On each pyramid level a Bayesian convolutional neural network is trained to compute class prediction and uncertainty on each pixel. This information is propagated top-down on the pyramid as a constraining mechanism from the above layers. To cope with local tissue and cell shape deformations geometric invariance is also introduced as a part of the model. The model achieves an F1-score of 82.6% on the MITOS ICPR-2012 test dataset when trained with samples from skin tissue. This is competitive with the current state of the art. In average a whole-slide is analyzed in less than 20 s. A new dataset of 8236 mitoses from skin tissue has been created to train our models.
机译：苏木精和曙红图像中的有丝分裂检测及其对mm2的定量是目前对某些类型的癌症（尤其是乳腺癌）最有价值的预后指标之一。在全幻灯片图像中，主要目标是检测其在整个图像上的存在。本文对整个幻灯片上的有丝分裂检测任务做出了一些贡献，以改善当前的技术水平和效率。提出了一种新的从粗到细的金字塔模型来检测有丝分裂。在每个金字塔级别，训练贝叶斯卷积神经网络以计算每个像素的类别预测和不确定性。这些信息作为约束机制从上而下在金字塔上自顶向下传播。为了应对局部组织和细胞形状变形，几何不变性也作为模型的一部分引入。使用皮肤组织样本训练后，该模型在MITOS ICPR-2012测试数据集上的F1分数达到82.6％。这与当前的技术水平相比具有竞争力。平均而言，整个滑道的分析时间少于20秒。已经创建了来自皮肤组织的8236个有丝分裂的新数据集来训练我们的模型。

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