Higher-resolution biopsy slice images reveal many details, which are widely used in medical practice. However, taking high-resolution slice images is more costly than taking low-resolution ones. In this paper, we propose a joint framework containing a novel transfer learning strategy and a deep super-resolution framework to generate high-resolution slice images from low-resolution ones. The super-resolution framework called SRFBN+ is proposed by modifying a state-of-the-art framework SRFBN. Specifically, the structure of the feedback block of SRFBN was modified to be more flexible. Besides, it is challenging to use typical transfer learning strategies directly for the tasks on slice images, as the patterns on different types of biopsy slice images are varying. To this end, we propose a novel transfer learning strategy, called Channel Fusion Transfer Learning (CF-Trans). CF-Trans builds a middle domain by fusing the data manifolds of the source domain and the target domain, serving as a springboard for knowledge transfer. Thus, in the transfer learning setting, SRFBN+ can be trained on the source domain and then the middle domain and finally the target domain. Experiments on biopsy slice images validate SRFBN+ works well in generating super-resolution slice images, and CF-Trans is an efficient transfer learning strategy.

中文翻译：

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用于活检切片图像的基于迁移学习的超分辨率显微镜：联合方法视角

更高分辨率的活检切片图像揭示了许多在医疗实践中广泛使用的细节。然而，获取高分辨率切片图像比获取低分辨率切片图像成本更高。在本文中，我们提出了一个联合框架，其中包含一种新的迁移学习策略和一个深度超分辨率框架，用于从低分辨率切片图像生成高分辨率切片图像。称为 SRFBN+ 的超分辨率框架是通过修改最先进的框架 SRFBN 提出的。具体来说，修改了 SRFBN 的反馈块的结构，使其更加灵活。此外，将典型的迁移学习策略直接用于切片图像上的任务是具有挑战性的，因为不同类型的活检切片图像上的模式各不相同。为此，我们提出了一种新颖的迁移学习策略，称为通道融合迁移学习（CF-Trans）。CF-Trans通过融合源域和目标域的数据流形构建中间域，作为知识转移的跳板。因此，在迁移学习设置中，SRFBN+ 可以在源域上进行训练，然后是中间域，最后是目标域。活检切片图像的实验验证了 SRFBN+ 在生成超分辨率切片图像方面效果良好，而 CF-Trans 是一种有效的迁移学习策略。