High-quality fluorescence imaging of biological systems is limited by
processes like photobleaching and phototoxicity, and also in many cases, by
limited access to the latest generations of microscopes. Moreover, low temporal
resolution can lead to a motion blur effect in living systems. Our work
presents a deep learning (DL) generative-adversarial approach to the problem of
obtaining high-quality (HQ) images based on their low-quality (LQ) equivalents.
We propose a generative-adversarial network (GAN) for contrast transfer between
two different separate microscopy systems: a confocal microscope (producing HQ
images) and a wide-field fluorescence microscope (producing LQ images). Our
model proves that such transfer is possible, allowing us to receive
HQ-generated images characterized by low mean squared error (MSE) values, high
structural similarity index (SSIM), and high peak signal-to-noise ratio (PSNR)
values. For our best model in the case of comparing HQ-generated images and
HQ-ground truth images, the median values of the metrics are 6x10-4, 0.9413,
and 31.87, for MSE, SSIM, and PSNR, respectively. In contrast, in the case of
comparison between LQ and HQ ground truth median values of the metrics are
equal to 0.0071, 0.8304, and 21.48 for MSE, SSIM, and PSNR respectively.
Therefore, we observe a significant increase ranging from 14% to 49% for SSIM
and PSNR respectively. These results, together with other single-system
cross-modality studies, provide proof of concept for further implementation of
a cross-system biological image quality enhancement.

通过生成对抗网络的方法，我们成功实现了从低质量图像到高质量图像的对比转换，产生了具有低均方误差、高结构相似性指数和高峰值信噪比的高质量图像，为跨系统的生物图像质量增强进一步的实施提供了概念验证。

基于生成对抗网络的跨系统生物图像质量增强：建立多机构显微镜合作网络的基础

Cross-system biological image quality enhancement based on the  generative adversarial network as a foundation for establishing a  multi-institute microscopy cooperative network

Domain generalization is critical for real-world applications of machine
learning models to microscopy images, including histopathology and fluorescence
imaging. Artifacts in histopathology arise through a complex combination of
factors relating to tissue collection and laboratory processing, as well as
factors intrinsic to patient samples. In fluorescence imaging, these artifacts
stem from variations across experimental batches. The complexity and subtlety
of these artifacts make the enumeration of data domains intractable. Therefore,
augmentation-based methods of domain generalization that require domain
identifiers and manual fine-tuning are inadequate in this setting. To overcome
this challenge, we introduce ContriMix, a domain generalization technique that
learns to generate synthetic images by disentangling and permuting the
biological content ("content") and technical variations ("attributes") in
microscopy images. ContriMix does not rely on domain identifiers or handcrafted
augmentations and makes no assumptions about the input characteristics of
images. We assess the performance of ContriMix on two pathology datasets
(Camelyon17-WILDS and a prostate cell classification dataset) and one
fluorescence microscopy dataset (RxRx1-WILDS). ContriMix outperforms current
state-of-the-art methods in all datasets, motivating its usage for microscopy
image analysis in real-world settings where domain information is hard to come
by.

本研究介绍了一种名为 ContriMix 的域通用技术，它通过分离并排列显微镜图像中的生物内容（“内容”）和技术变化（“属性”）来学习生成合成图像，以实现对组织病理学和荧光成像等显微图像的域通用化，结果表明 ContriMix 在各种数据集上均优于当前的最先进方法，可用于显微图像分析等领域。

ContriMix: 显微镜图像分析中的领域泛化内容和属性的无监督分离

ContriMix: Unsupervised disentanglement of content and attribute for  domain generalization in microscopy image analysis

This article presents a review of computational methods for connectivity
inference from neural activity data derived from multi-electrode recordings or
fluorescence imaging. We first identify biophysical and technical challenges in
connectivity inference along the data processing pipeline. We then review
connectivity inference methods based on two major mathematical foundations,
namely, descriptive model-free approaches and generative model-based
approaches. We investigate representative studies in both categories and
clarify which challenges have been addressed by which method. We further
identify critical open issues and possible research directions.

本文综述了从多电极记录或荧光成像获得的神经活动数据中推断连通性的计算方法，探讨了描述性无模型方法和生成性基于模型方法两个数学基础，并分析了代表性研究，最后指出了关键性的未解决问题和可能的研究方向。