Annu Int Conf IEEE Eng Med Biol Soc. 2022 Jul;2022:1891-1894. doi: 10.1109/EMBC48229.2022.9871819.
Breast conserving surgery aims at the complete removal of malignant lesions while minimizing healthy tissue loss. To ensure the balance between complete resection of the cancer and conservation of healthy tissue, intra-operative margin assessment is necessary. Deep ultraviolet (DUV) fluorescence scanning microscope provides fast whole-surface-imaging (WSI) of excised tissue with contrast between malignant and normal tissues. Then, an automated breast cancer classification method on DUV images is required for intra-operative margin assessment. Deep learning shows the promising results in breast cancer classification, but limited DUV image dataset poses overfitting challenge to train the robust network. To tackle this challenge, we partition the DUV WSI image into small patches and extract pathological features for each patch from a pre-trained network using a transfer learning approach. We feed pathological features into a decision-tree-based classifier and fuse patch-level classification results based on regional importance to determine malignant or benign WSI. Experimental results on 60 DUV images show that our proposed method outperforms the standard deep learning classification in terms of improving the classification performance and identifying cancerous regions.
保乳手术旨在彻底切除恶性病变，同时最大限度地减少健康组织的损失。为了在癌症的完全切除和健康组织的保留之间取得平衡，需要进行术中切缘评估。深紫外（DUV）荧光扫描显微镜可快速对切除组织进行全表面成像（WSI），使恶性组织和正常组织之间具有对比度。然后，需要一种基于 DUV 图像的自动乳腺癌分类方法来进行术中切缘评估。深度学习在乳腺癌分类中显示出了很有前景的结果，但有限的 DUV 图像数据集给训练稳健的网络带来了过拟合的挑战。为了解决这个挑战，我们将 DUV WSI 图像分割成小的补丁，并使用迁移学习方法从预训练的网络中提取每个补丁的病理特征。我们将病理特征输入到基于决策树的分类器中，并根据区域重要性融合补丁级别的分类结果，以确定 WSI 是恶性的还是良性的。在 60 张 DUV 图像上的实验结果表明，与标准的深度学习分类方法相比，我们提出的方法在提高分类性能和识别癌性区域方面表现更好。
