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

Computational cytology is a critical, rapid-developing, yet challenging topic
in the field of medical image computing which analyzes the digitized cytology
image by computer-aided technologies for cancer screening. Recently, an
increasing number of deep learning (DL) algorithms have made significant
progress in medical image analysis, leading to the boosting publications of
cytological studies. To investigate the advanced methods and comprehensive
applications, we survey more than 120 publications of DL-based cytology image
analysis in this article. We first introduce various deep learning methods,
including fully supervised, weakly supervised, unsupervised, and transfer
learning. Then, we systematically summarize the public datasets, evaluation
metrics, versatile cytology image analysis applications including
classification, detection, segmentation, and other related tasks. Finally, we
discuss current challenges and potential research directions of computational
cytology.

本文调查了 120 多篇基于深度学习的细胞学图像分析研究，介绍了全监督、弱监督、无监督和迁移学习等深度学习方法，并系统总结了公共数据集、评估指标、分类、检测等多种深度学习应用，探讨了细胞学计算的挑战和未来研究方向。