Histological staining of tissue biopsies, especially hematoxylin and eosin
(H&E) staining, serves as the benchmark for disease diagnosis and comprehensive
clinical assessment of tissue. However, the process is laborious and
time-consuming, often limiting its usage in crucial applications such as
surgical margin assessment. To address these challenges, we combine an emerging
3D quantitative phase imaging technology, termed quantitative oblique back
illumination microscopy (qOBM), with an unsupervised generative adversarial
network pipeline to map qOBM phase images of unaltered thick tissues (i.e.,
label- and slide-free) to virtually stained H&E-like (vH&E) images. We
demonstrate that the approach achieves high-fidelity conversions to H&E with
subcellular detail using fresh tissue specimens from mouse liver, rat
gliosarcoma, and human gliomas. We also show that the framework directly
enables additional capabilities such as H&E-like contrast for volumetric
imaging. The quality and fidelity of the vH&E images are validated using both a
neural network classifier trained on real H&E images and tested on virtual H&E
images, and a user study with neuropathologists. Given its simple and low-cost
embodiment and ability to provide real-time feedback in vivo, this deep
learning-enabled qOBM approach could enable new workflows for histopathology
with the potential to significantly save time, labor, and costs in cancer
screening, detection, treatment guidance, and more.

该研究结合 3D 量化相位成像技术和非监督生成对抗网络管道，将未修改的厚组织的 qOBM 相位图像映射到类似于 H＆E 的 vH＆E 图像。这种有望显著节省肿瘤筛查、检测、治疗指导等时间、费用和人力的深度学习 qOBM 方法能够为组织病理学的新工作流程提供新的可能性。

采用 3D Epi - 模式定量相位成像和虚拟 H&E 染色的无标签和无玻片组织组织学

Label- and slide-free tissue histology using 3D epi-mode quantitative  phase imaging and virtual H&E staining

Deep learning-based methods have achieved remarkable success in image
restoration and enhancement, but are they still competitive when there is a
lack of paired training data? As one such example, this paper explores the
low-light image enhancement problem, where in practice it is extremely
challenging to simultaneously take a low-light and a normal-light photo of the
same visual scene. We propose a highly effective unsupervised generative
adversarial network, dubbed EnlightenGAN, that can be trained without
low/normal-light image pairs, yet proves to generalize very well on various
real-world test images. Instead of supervising the learning using ground truth
data, we propose to regularize the unpaired training using the information
extracted from the input itself, and benchmark a series of innovations for the
low-light image enhancement problem, including a global-local discriminator
structure, a self-regularized perceptual loss fusion, and attention mechanism.
Through extensive experiments, our proposed approach outperforms recent methods
under a variety of metrics in terms of visual quality and subjective user
study. Thanks to the great flexibility brought by unpaired training,
EnlightenGAN is demonstrated to be easily adaptable to enhancing real-world
images from various domains. The code is available at
https://github.com/yueruchen/EnlightenGAN

本文提出了一种名为 EnlightenGAN 的高效的非监督生成对抗网络，采用了无配对的训练数据来提高低光图像的质量，通过全局局部鉴别器结构，自我规范化的知觉损失融合和注意机制等方法，在不同种类的图像测试中表现优于其他方法。