论文标题
使用转移学习和广泛的几何数据增强的多个神经网络的组合，用于评估组织病理学图像中的细胞分数
Combination of multiple neural networks using transfer learning and extensive geometric data augmentation for assessing cellularity scores in histopathology images
论文作者
论文摘要
当前，组织样品中癌细胞的分类是病理学家进行的手动过程。正确确定癌症细胞性的过程可能是耗时的。特别是，由于它们所表现出的准确性和表现，深度学习（DL）技术越来越流行，这可以与病理学家相提并论。这项工作调查了两种DL方法评估SPIE-AAPM-NCI BRASISTATHQ挑战数据集中整个幻灯片图像（WSI）中癌细胞性的功能。使用修改后的Kendall Tau-B预测概率公制分析了训练对增强数据的影响，并通过称为平均预测概率PK进行了分析多个架构到单个网络的组合。深入，传递的卷积神经网络（CNN）的插入用作基线，平均PK值为0.884，显示出与病理学家达到的0.83的平均PK值的提高。然后，对网络进行了其他训练数据集的培训，该数据集旋转1至360度之间，PK的峰值增加了4.2％。由InceptionV3网络和VGG16组成的附加体系结构（浅，转移学到的CNN）合并为并行体系结构。该平行体系结构的基线平均PK值为0.907，这在统计学上分别比任何一个架构的性能都显着改善（未配对t检验的P <0.0001）。
Classification of cancer cellularity within tissue samples is currently a manual process performed by pathologists. This process of correctly determining cancer cellularity can be time intensive. Deep Learning (DL) techniques in particular have become increasingly more popular for this purpose, due to the accuracy and performance they exhibit, which can be comparable to the pathologists. This work investigates the capabilities of two DL approaches to assess cancer cellularity in whole slide images (WSI) in the SPIE-AAPM-NCI BreastPathQ challenge dataset. The effects of training on augmented data via rotations, and combinations of multiple architectures into a single network were analyzed using a modified Kendall Tau-b prediction probability metric known as the average prediction probability PK. A deep, transfer learned, Convolutional Neural Network (CNN) InceptionV3 was used as a baseline, achieving an average PK value of 0.884, showing improvement from the average PK value of 0.83 achieved by pathologists. The network was then trained on additional training datasets which were rotated between 1 and 360 degrees, which saw a peak increase of PK up to 4.2%. An additional architecture consisting of the InceptionV3 network and VGG16, a shallow, transfer learned CNN, was combined in a parallel architecture. This parallel architecture achieved a baseline average PK value of 0.907, a statistically significantly improvement over either of the architectures' performances separately (p<0.0001 by unpaired t-test).
