Featurizing microscopy images for use in biological research remains a
significant challenge, especially for large-scale experiments spanning millions
of images. This work explores the scaling properties of weakly supervised
classifiers and self-supervised masked autoencoders (MAEs) when training with
increasingly larger model backbones and microscopy datasets. Our results show
that ViT-based MAEs outperform weakly supervised classifiers on a variety of
tasks, achieving as much as a 11.5% relative improvement when recalling known
biological relationships curated from public databases. Additionally, we
develop a new channel-agnostic MAE architecture (CA-MAE) that allows for
inputting images of different numbers and orders of channels at inference time.
We demonstrate that CA-MAEs effectively generalize by inferring and evaluating
on a microscopy image dataset (JUMP-CP) generated under different experimental
conditions with a different channel structure than our pretraining data
(RPI-93M). Our findings motivate continued research into scaling
self-supervised learning on microscopy data in order to create powerful
foundation models of cellular biology that have the potential to catalyze
advancements in drug discovery and beyond.

在生物研究中，将显微镜图像分析成特征仍然是一个重大挑战。本研究探讨了弱监督分类器和自监督掩码自编码器（MAEs）在使用越来越大的模型和显微镜数据集进行训练时的伸缩性能。我们的研究结果表明，基于 ViT 的 MAEs 在多项任务上优于弱监督分类器，在从公共数据库中获取的已知生物关系的召回中相对改进了 11.5%。此外，我们还开发了一种新的通道无关的 MAE 架构（CA-MAE），可以在推理时输入具有不同通道数和顺序的图像。我们证明了 CA-MAEs 通过在具有不同实验条件和通道结构的显微镜图像数据集（JUMP-CP）上进行推断和评估的方法有效地泛化。我们的发现促使继续在显微镜数据上进行自监督学习的规模扩展研究，以创建能够催化药物发现等领域进展的细胞生物学强大基础模型。

显微镜遮蔽自编码器是可扩展的细胞生物学学习模型

Masked Autoencoders for Microscopy are Scalable Learners of Cellular  Biology

In this work, we describe a method for large-scale 3D cell-tracking through a
segmentation selection approach. The proposed method is effective at tracking
cells across large microscopy datasets on two fronts: (i) It can solve problems
containing millions of segmentation instances in terabyte-scale 3D+t datasets;
(ii) It achieves competitive results with or without deep learning, which
requires 3D annotated data, that is scarce in the fluorescence microscopy
field. The proposed method computes cell tracks and segments using a hierarchy
of segmentation hypotheses and selects disjoint segments by maximizing the
overlap between adjacent frames. We show that this method achieves
state-of-the-art results in 3D images from the cell tracking challenge and has
a faster integer linear programming formulation. Moreover, our framework is
flexible and supports segmentations from off-the-shelf cell segmentation models
and can combine them into an ensemble that improves tracking. The code is
available this https URL

通过分割选择方法实现大规模三维细胞追踪，该方法在两个方面对大规模显微数据集中的细胞追踪问题具有有效性：（i）能够解决拥有数百万分割实例的 TB 级三维时间数据集的问题；（ii）能够在不依赖稀缺的三维标注数据的情况下与使用深度学习的方法达到竞争水平。该方法通过分割假设的层次结构计算细胞轨迹和分割，并通过最大化相邻帧之间的重叠来选择不相交的分割。证明该方法在细胞追踪挑战中实现了最先进的结果，并且拥有更快的整数线性规划公式。此外，我们的框架灵活性强，支持来自现成细胞分割模型的分割，并能够将它们组合成一个提高追踪效果的整体。代码可以在此链接中找到：https:// 此链接网址

大规模多假设细胞跟踪基于超指标轮廓映射

Large-Scale Multi-Hypotheses Cell Tracking Using Ultrametric Contours  Maps

Today, Convolutional Neural Networks (CNNs) are the leading method for image
denoising. They are traditionally trained on pairs of images, which are often
hard to obtain for practical applications. This motivates self-supervised
training methods such as Noise2Void~(N2V) that operate on single noisy images.
Self-supervised methods are, unfortunately, not competitive with models trained
on image pairs. Here, we present 'Probabilistic Noise2Void' (PN2V), a method to
train CNNs to predict per-pixel intensity distributions. Combining these with a
suitable description of the noise, we obtain a complete probabilistic model for
the noisy observations and true signal in every pixel. We evaluate PN2V on
publicly available microscopy datasets, under a broad range of noise regimes,
and achieve competitive results with respect to supervised state-of-the-art
methods.

本研究提出了一种名为 Probabilistic Noise2Void 的卷积神经网络训练方法，可用于单一噪声图像的去噪。通过与噪声的合适描述相结合，可以得到每个像素的噪声观测和真实信号的完整概率模型。在各种噪声情况下评估了该方法，应用于公开的显微镜数据集，其表现和有监督的现有方法相当。