Generative Modeling of Histology Tissue Reduces Human Annotation Effort for Segmentation Model Development.
Generative Modeling of Histology Tissue Reduces Human Annotation Effort for Segmentation Model Development.
复制标题
组织学组织的生成建模减少了分割模型开发的人工注释工作。
DOI：
10.1117/12.2655282
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发表时间：
2023
期刊：
影响因子：
--
通讯作者：
Sarder,Pinaki
中科院分区：
文献类型：
--
作者：
Lutnick,Brendon;Lucarelli,Nicholas;Sarder,Pinaki
组织学组织全侧面图像的分割是组织分析的重要步骤。给定足够的注释训练数据，现代神经网络能够准确地进行可再现的分割;然而，训练数据集的注释是耗时的。诸如人在回路注释之类的技术试图减少这种注释负担，但仍然需要大量的初始注释。半监督学习--一种利用标记和未标记数据来学习特征的技术--已经显示出减轻注释负担的希望。为了实现这一目标，我们采用了最近发表的半监督方法，EQUETGAN，从肾活检图像的肾小球分割。我们比较了使用EQUETGAN和传统注释训练的模型的性能，并表明EQUETGAN显着减少了开发高性能分割模型所需的注释量。我们还探索了MacketGAN在迁移学习中的有用性，发现当使用有限数量的整个幻灯片图像进行训练时，这种方法大大提高了性能。
Segmentation of histology tissue whole side images is an important step for tissue analysis. Given enough annotated training data, modern neural networks are capable of accurate reproducible segmentation; however, the annotation of training datasets is time consuming. Techniques such as human-in-the-loop annotation attempt to reduce this annotation burden, but still require vast initial annotation. Semi-supervised learning—a technique which leverages both labeled and unlabeled data to learn features—has shown promise for easing the burden of annotation. Towards this goal, we employ a recently published semi-supervised method, datasetGAN, for the segmentation of glomeruli from renal biopsy images. We compare the performance of models trained using datasetGAN and traditional annotation and show that datasetGAN significantly reduces the amount of annotation required to develop a highly performing segmentation model. We also explore the usefulness of datasetGAN for transfer learning and find that this method greatly enhances the performance when a limited number of whole slide images are used for training.