Breast cancer is the leading cause of cancer-related deaths among women worldwide, and early detection and treatment has been shown to significantly reduce fatality rates from severe illness. Moreover, determination of the human epidermal growth factor receptor-2 (HER2) gene amplification by Fluorescence in situ hybridization (FISH) and Dual in situ hybridization (DISH) is critical for the selection of appropriate breast cancer patients for HER2-targeted therapy. However, visual examination of microscopy is time-consuming, subjective and poorly reproducible due to high inter-observer variability among pathologists and cytopathologists. The lack of consistency in identifying carcinoma-like nuclei has led to divergences in the calculation of sensitivity and specificity. This manuscript introduces a highly efficient deep learning method with low computing cost. The experimental results demonstrate that the proposed framework achieves high precision and recall on three essential clinical applications, including breast cancer diagnosis and human epidermal receptor factor 2 (HER2) amplification detection on FISH and DISH slides for HER2 target therapy. Furthermore, the proposed method outperforms the majority of the benchmark methods in terms of IoU by a significant margin (p<0.001) on three essential clinical applications. Importantly, run time analysis shows that the proposed method obtains excellent segmentation results with notably reduced time for Artificial intelligence (AI) training (16.93%), AI inference (17.25%) and memory usage (18.52%), making the proposed framework feasible for practical clinical usage.
乳腺癌是全球女性癌症相关死亡的首要原因，早期发现和治疗已被证实能显著降低严重疾病的致死率。此外，通过荧光原位杂交（FISH）和双原位杂交（DISH）检测人表皮生长因子受体-2（HER2）基因扩增，对于筛选适合HER2靶向治疗的乳腺癌患者至关重要。然而，由于病理学家和细胞病理学家之间存在显著的观察者间变异性，显微镜图像的视觉判读耗时较长，且具有主观性和较差的可重复性。对癌细胞样细胞核识别标准的不一致，导致灵敏度和特异性计算结果产生分歧。本文介绍了一种计算成本低、效率高的深度学习方法。实验结果表明，所提出的框架在三个关键临床应用中实现了高准确率和召回率，包括乳腺癌诊断以及FISH和DISH切片上HER2靶向治疗相关扩增的检测。此外，在三个关键临床应用中，所提出方法在交并比（IoU）指标上显著优于多数基准方法（p<0.001）。重要的是，运行时分析显示，该方法在人工智能（AI）训练时间（16.93%）、AI推理时间（17.25%）和内存占用（18.52%）方面均显著减少，同时保持了优异的分割结果，使所提出的框架具备实际临床应用的可行性。
Efficient Convolution Network to Assist Breast Cancer Diagnosis and Target Therapy
肿瘤(癌症)患者之家