小柯机器人

复旦大学颜波团队预训练出一个基础模型用于基于荧光显微镜的通用图像复原。相关论文于2024年4月12日在线发表在《自然—方法学》杂志上。

研究人员试图提高通用性，并探索将预训练基础模型应用于基于荧光显微镜的图像复原的潜力。研究人员提供了一个通用的基于荧光显微镜的图像复原（UniFMIR）模型来解决不同的图像复原问题，结果表明UniFMIR具有更高的图像复原精度、更好的通用性和更强的通用性。

在涵盖多种显微成像模式和生物样本的五项任务和14个数据集上进行的演示表明，经过预训练的UniFMIR可以通过微调将知识有效地转移到特定情况下，发现清晰的纳米级生物分子结构并促进高质量成像。这项工作有望为基于荧光显微镜的图像修复带来新的研究灵感和亮点。

据悉，受益于深度学习技术，基于荧光显微镜的图像复原在生命科学领域受到广泛关注，并取得了重大进展。然而，目前大多数针对特定任务的方法对基于荧光显微镜的不同图像复原问题的普适性有限。

附：英文原文

Title: Pretraining a foundation model for generalizable fluorescence microscopy-based image restoration

Author: Ma, Chenxi, Tan, Weimin, He, Ruian, Yan, Bo

Issue&Volume: 2024-04-12

Abstract: Fluorescence microscopy-based image restoration has received widespread attention in the life sciences and has led to significant progress, benefiting from deep learning technology. However, most current task-specific methods have limited generalizability to different fluorescence microscopy-based image restoration problems. Here, we seek to improve generalizability and explore the potential of applying a pretrained foundation model to fluorescence microscopy-based image restoration. We provide a universal fluorescence microscopy-based image restoration (UniFMIR) model to address different restoration problems, and show that UniFMIR offers higher image restoration precision, better generalization and increased versatility. Demonstrations on five tasks and 14 datasets covering a wide range of microscopy imaging modalities and biological samples demonstrate that the pretrained UniFMIR can effectively transfer knowledge to a specific situation via fine-tuning, uncover clear nanoscale biomolecular structures and facilitate high-quality imaging. This work has the potential to inspire and trigger new research highlights for fluorescence microscopy-based image restoration.

DOI: 10.1038/s41592-024-02244-3

Source: https://www.nature.com/articles/s41592-024-02244-3

Nature Methods：《自然—方法学》，创刊于2004年。隶属于施普林格·自然出版集团，最新IF：47.99
官方网址：https://www.nature.com/nmeth/
投稿链接：https://mts-nmeth.nature.com/cgi-bin/main.plex