Weigert lab 发了新方法，不用标注就能做细胞分割和分类，效果跟监督方法差不多，代码已经开源了。
该论文提出一种无监督方法，用粗到细的路由金字塔将像素与稀疏潜在源关联，直接从无标注显微图像得到实例掩码。潜在源同时编码细胞形态，免去人工标注和分阶段处理。在多种细胞形态和成像模态上，实例分割性能与监督方法相当。论文还展示了扰动条件下细胞表型的生成建模。代码和模型权重已在 GitHub 上公开。
Unsupervised Learning of Cell Instances with Generative Routing Pyramids
Identifying and representing object instances such as cells or nuclei is a common task in microscopy image analysis. Established machine learning workflows typically use supervised detection or segmentation followed by feature extraction or classification, which requires manual annotations and treats instance segmentation and cell representation as separate stages. We describe a new unsupervised method for cell instance segmentation and phenotypic classification from unlabeled microscopy images. Our method is based on reconstructing each image using a coarse-to-fine routing pyramid that associates pixels with spatially sparse latent sources. The resulting pixel-to-latent associations yield instance masks, while the source latents encode cell morphology. We demonstrate competitive performance in instance segmentation across diverse cell morphologies and imaging modalities, as well as generative modeling of cellular phenotypes under perturbations. Source code and checkpoints are available at https://github.com/weigertlab/routing-pyramids.