论文标题
深层神经网络的多任务预培训用于数字病理
Multi-task pre-training of deep neural networks for digital pathology
论文作者
论文摘要
在这项工作中，我们研究了多任务学习，作为数字病理学分类任务的预训练模型的一种方式。这是因为多年来，社区已经发布了许多中型数据集，而在域中没有类似于Imagenet的大规模数据集。我们首先将许多数字病理数据集组装成22个分类任务和近900K图像的池。然后，我们提出了一个简单的体系结构和培训方案，用于创建可转移的模型以及一个可靠的评估和选择协议，以评估我们的方法。根据目标任务，我们表明，用作特征提取器的模型要么比ImageNet预训练的模型显着改善，要么提供可比的性能。微调可以提高特征提取的性能，并能够恢复缺乏成像网特征的特异性，因为两种预训练源都产生了可比的性能。
In this work, we investigate multi-task learning as a way of pre-training models for classification tasks in digital pathology. It is motivated by the fact that many small and medium-size datasets have been released by the community over the years whereas there is no large scale dataset similar to ImageNet in the domain. We first assemble and transform many digital pathology datasets into a pool of 22 classification tasks and almost 900k images. Then, we propose a simple architecture and training scheme for creating a transferable model and a robust evaluation and selection protocol in order to evaluate our method. Depending on the target task, we show that our models used as feature extractors either improve significantly over ImageNet pre-trained models or provide comparable performance. Fine-tuning improves performance over feature extraction and is able to recover the lack of specificity of ImageNet features, as both pre-training sources yield comparable performance.
