Deep neural networks have introduced significant advancements in the field of machine learning-based analysis of digital pathology images including prostate tissue images. With the help of transfer learning, classification and segmentation performance of neural network models have been further increased. However, due to the absence of large, extensively annotated, publicly available prostate histopathology datasets, several previous studies employ datasets from well-studied computer vision tasks such as ImageNet dataset. In this work, we propose a transfer learning scheme from breast histopathology images to improve prostate cancer detection performance. We validate our approach on annotated prostate whole slide images by using a publicly available breast histopathology dataset as pre-training. We show that the proposed cross-cancer approach outperforms transfer learning from ImageNet dataset.
翻译:深神经网络在对包括前列腺组织图象在内的数字病理学图像进行机器学习分析方面取得了显著进展,在神经网络模型的传导学习、分类和分解性能帮助下,神经网络模型的传导、分类和分解性能进一步提高,然而,由于缺乏大量、广泛注解、公开可得的前列腺病理学数据集,先前的几项研究采用了从图象网数据集等经过仔细研究的计算机视觉任务中产生的数据集。在这项工作中,我们提议从乳房病理学图象中转学计划,以提高前列腺癌症检测性能。我们确认我们对注解前列腺整片图象的方法,在培训前使用公开提供的乳腺病理学数据集。我们显示,拟议的跨癌症方法超越了从图象网数据集中学习的系统转移。