Abstract
Generating realistic synthetic microscopy images is critical for training deep learning models in label-scarce environments, such as cell counting with many cells per image. However, traditional domain adaptation methods often struggle to bridge the domain gap when synthetic images lack the complex textures and visual patterns of real samples. In this work, we adapt the Inversion-Based Style Transfer (InST) framework originally designed for artistic style transfer to biomedical microscopy images. Our method combines latent-space Adaptive Instance Normalization with stochastic inversion in a diffusion model to transfer the style from real fluorescence microscopy images to synthetic ones, while weakly preserving content structure. We evaluate the effectiveness of our InST-based synthetic dataset for downstream cell counting by pre-training and fine-tuning EfficientNet-B0 models on various data sources, including real data, hard-coded synthetic data, and the public Cell200-s dataset. Models trained with our InST-synthesized images achieve up to 37\% lower Mean Absolute Error (MAE) compared to models trained on hard-coded synthetic data, and a 52\% reduction in MAE compared to models trained on Cell200-s (from 53.70 to 25.95 MAE). Notably, our approach also outperforms models trained on real data alone (25.95 vs. 27.74 MAE). Further improvements are achieved when combining InST-synthesized data with lightweight domain adaptation techniques such as DACS with CutMix. These findings demonstrate that InST-based style transfer most effectively reduces the domain gap between synthetic and real microscopy data. Our approach offers a scalable path for enhancing cell counting performance while minimizing manual labeling effort. The source code and resources are publicly available at: this https URL.
Abstract (translated)
生成逼真的合成显微图像对于在标签稀缺的环境下训练深度学习模型（例如，每张图像中有大量细胞的情况下的计数任务）至关重要。然而，传统的领域适应方法往往难以跨越合成图像缺乏真实样本中的复杂纹理和视觉模式的领域差距。在这项工作中，我们将原本用于艺术风格迁移的基于逆向的样式转换(Inversion-Based Style Transfer, InST)框架应用于生物医学显微镜图像。我们的方法结合了潜在空间自适应实例归一化与扩散模型中的随机反转，以从真实荧光显微图像中传递风格到合成图像上，并弱化地保持内容结构。 我们通过在不同数据源（包括真实数据、硬编码的合成数据以及公共Cell200-s数据集）上对EfficientNet-B0模型进行预训练和微调来评估基于InST的合成数据集用于下游细胞计数的有效性。使用我们的InST生成图像训练得到的模型相比单纯使用硬编码合成数据训练的模型，平均绝对误差(Mean Absolute Error, MAE)最多可减少37%，与Cell200-s上训练的模型相比则能降低52%（MAE从53.70降至25.95）。值得注意的是，我们的方法甚至超过了单纯基于真实数据训练得到的结果（25.95 vs. 27.74 MAE）。 通过将InST合成的数据与轻量级领域适应技术相结合（如DACS结合CutMix），还可以进一步提高性能。这些发现表明，基于InST的风格转换最有效地减少了合成和真实显微数据之间的域差距。我们的方法为在减少人工标注工作的同时提升细胞计数性能提供了一条可扩展的途径。源代码和资源可在以下网址获取：[this https URL] (请将 [ ] 替换为实际链接地址)。
URL
https://arxiv.org/abs/2512.11763
