核心针生物检测（CNB）对乳腺癌初步诊断起着至关重要的作用。然而，复杂的组织处理和全球病理学家短缺阻碍了传统的组织病理学在新鲜活检中进行及时诊断。在这项工作中，我们通过将无标记激发拉曼散射（SRS）显微镜与弱监督学习集成，开发了一个完整的数字平台，用于对无标记乳腺CNB进行快速和自动化的癌症诊断。方法：我们首先将SRS成像结果与邻近的冰冻组织切片上的标准苏木精和伊红染色（H＆E染色）进行比较。然后，通过SRS成像来显示诊断组织建筑。接下来，使用弱监督学习，即多实例学习（MIL）模型来评估区分良性和恶性病例的能力，并与监督学习模型的性能进行比较。最后，使用梯度加权类激活映射（Grad-CAM）和语义分割来以高效的方式解决良性/恶性区域的空间分辨率。结果：我们验证了SRS在冰冻薄切片和新鲜未经处理的活检中揭示乳腺癌的基本组织学特征的能力，并生成了与H＆E染色相关的组织结构。此外，我们证明了弱监督MIL模型可以实现比监督学习更好的分类性能，在61个活检标本中达到95％的诊断准确度。此外，Grad-CAM允许训练有素的MIL模型可视化CNB内部的组织学异质性。结论：我们的结果表明，MIL辅助SRS显微镜技术可在组织学异质性的乳腺CNB上提供快速准确诊断，并有可能帮助进一步的患者管理。 ©作者

Core-needle biopsy (CNB) plays a vital role in the initial diagnosis of breast cancer. However, the complex tissue processing and global shortage of pathologists have hindered traditional histopathology from timely diagnosis on fresh biopsies. In this work, we developed a full digital platform by integrating label-free stimulated Raman scattering (SRS) microscopy with weakly-supervised learning for rapid and automated cancer diagnosis on un-labelled breast CNB. Methods: We first compared the results of SRS imaging with standard hematoxylin and eosin (H&E) staining on adjacent frozen tissue sections. Then fresh unprocessed biopsy tissues were imaged by SRS to reveal diagnostic histoarchitectures. Next, weakly-supervised learning, i.e., the multi-instance learning (MIL) model was conducted to evaluate the ability to differentiate between benign and malignant cases, and compared with the performance of supervised learning model. Finally, gradient-weighted class activation mapping (Grad-CAM) and semantic segmentation were performed to spatially resolve benign/malignant areas with high efficiency. Results: We verified the ability of SRS in revealing essential histological hallmarks of breast cancer in both thin frozen sections and fresh unprocessed biopsy, generating histoarchitectures well correlated with H&E staining. Moreover, we demonstrated that weakly-supervised MIL model could achieve superior classification performance to supervised learnings, reaching diagnostic accuracy of 95% on 61 biopsy specimens. Furthermore, Grad-CAM allowed the trained MIL model to visualize the histological heterogeneity within the CNB. Conclusion: Our results indicate that MIL-assisted SRS microscopy provides rapid and accurate diagnosis on histologically heterogeneous breast CNB, and could potentially help the subsequent management of patients.© The author(s).