Neural Stain-Style Transfer Learning using GAN for Histopathological Images
Seoul National University（首尔国立大学）；Korea University（高丽大学）；Yonsei University（延世大学）；KAIST（韩国科学技术院）
英文摘要：
Performance of data-driven network for tumor classification varies with stain-style of histopathological images. This article proposes the stain-style transfer (SST) model based on conditional generative adversarial networks (GANs) which is to learn not only the certain color distribution but also the corresponding histopathological pattern. Our model considers feature-preserving loss in addition to well-known GAN loss. Consequently our model does not only transfers initial stain-styles to the desired one but also prevent the degradation of tumor classifier on transferred images. The model is examined using the CAMELYON16 dataset.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
