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Vascular Biology Program and Department of Surgery, Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts 02115, USA.
Data Science Program, Worcester Polytechnic Institute, Worcester, Massachusetts 01609, USA.
APL Bioeng. 2025 Jul 23;9(3):036107. doi: 10.1063/5.0246495. eCollection 2025 Sep.
Lens-free digital in-line holography (LDIH) provides a large field-of-view at micrometer-scale resolution, making it a promising tool for high-throughput cellular analysis. However, the complexity of diffraction images (holograms) produced by LDIH presents challenges for human interpretation and requires time-consuming computational reconstruction, often leading to artifacts and information loss. To address these issues, we present HoloNet, a novel deep learning architecture specifically designed for direct analysis of diffraction images in cellular diagnostics. Tailored to the unique characteristics of diffraction images, HoloNet captures multi-scale features, enabling it to outperform conventional convolutional neural networks in recognizing well-defined regions within complex holograms. HoloNet classifies breast cancer cell types with high precision and quantifies molecular marker intensities using raw diffraction images of cells stained with ER/PR and HER2. Additionally, HoloNet has proven effective in transfer learning applications, accurately classifying breast cancer cell lines and discovering previously unidentified subtypes through unsupervised learning. By integrating computational imaging with deep learning, HoloNet offers a robust solution to the challenges of holographic data analysis, significantly improving the accuracy and explainability of cellular diagnostics.
无透镜数字同轴全息术（LDIH）在微米级分辨率下提供大视野，使其成为高通量细胞分析的一个有前途的工具。然而，LDIH产生的衍射图像（全息图）的复杂性给人工解读带来了挑战，并且需要耗时的计算重建，这常常导致伪像和信息丢失。为了解决这些问题，我们提出了HoloNet，这是一种专门为细胞诊断中衍射图像的直接分析而设计的新型深度学习架构。针对衍射图像的独特特征进行定制，HoloNet能够捕捉多尺度特征，使其在识别复杂全息图中定义明确的区域方面优于传统卷积神经网络。HoloNet使用用雌激素受体/孕激素受体（ER/PR）和人表皮生长因子受体2（HER2）染色的细胞的原始衍射图像，高精度地对乳腺癌细胞类型进行分类，并对分子标记强度进行量化。此外，HoloNet在迁移学习应用中已被证明是有效的，通过无监督学习准确地对乳腺癌细胞系进行分类并发现以前未识别的亚型。通过将计算成像与深度学习相结合，HoloNet为全息数据分析的挑战提供了一个强大的解决方案，显著提高了细胞诊断的准确性和可解释性。
