论文标题
细分器：无监督表示学习和模块化图像的实例分割
CellSegmenter: unsupervised representation learning and instance segmentation of modular images
论文作者
论文摘要
我们介绍了CellSementerer，这是一个结构化的深层生成模型，也是一个用于无监督表示学习和实例分割任务的摊销推理框架。所提出的推理算法是卷积和并行的，没有任何复发机制，并且能够解决对象对象的闭塞，同时同时对远处的非闭合对象进行独立处理。这导致了非常快速的训练时间，同时允许外推到任意数量的实例。我们进一步介绍了一种透明的后正规化策略，该策略鼓励场景重建具有最少的本地化对象和低复杂的背景。我们在具有结构化背景的具有挑战性的合成多机数据集上评估了我们的方法，并仅使用几百个培训时代获得了几乎完美的准确性。最后，我们显示了针对细胞核成像数据集获得的分割结果，证明了我们方法提供高质量分割的能力，同时还处理涉及大量实例的现实用例。
We introduce CellSegmenter, a structured deep generative model and an amortized inference framework for unsupervised representation learning and instance segmentation tasks. The proposed inference algorithm is convolutional and parallelized, without any recurrent mechanisms, and is able to resolve object-object occlusion while simultaneously treating distant non-occluding objects independently. This leads to extremely fast training times while allowing extrapolation to arbitrary number of instances. We further introduce a transparent posterior regularization strategy that encourages scene reconstructions with fewest localized objects and a low-complexity background. We evaluate our method on a challenging synthetic multi-MNIST dataset with a structured background and achieve nearly perfect accuracy with only a few hundred training epochs. Finally, we show segmentation results obtained for a cell nuclei imaging dataset, demonstrating the ability of our method to provide high-quality segmentations while also handling realistic use cases involving large number of instances.
