Breast cancer is one of the leading causes of cancer mortality. Breast cancer
patients in developing countries, especially sub-Saharan Africa, South Asia,
and South America, suffer from the highest mortality rate in the world. One
crucial factor contributing to the global disparity in mortality rate is long
delay of diagnosis due to a severe shortage of trained pathologists, which
consequently has led to a large proportion of late-stage presentation at
diagnosis. The delay between the initial development of symptoms and the
receipt of a diagnosis could stretch upwards 15 months. To tackle this critical
healthcare disparity, this research has developed a deep learning-based
diagnosis system for metastatic breast cancer that can achieve high diagnostic
accuracy as well as computational efficiency. Based on our evaluation, the
MobileNetV2-based diagnostic model outperformed the more complex VGG16,
ResNet50 and ResNet101 models in diagnostic accuracy, model generalization, and
model training efficiency. The visual comparisons between the model prediction
and ground truth have demonstrated that the MobileNetV2 diagnostic models can
identify very small cancerous nodes embedded in a large area of normal cells
which is challenging for manual image analysis. Equally Important, the light
weighted MobleNetV2 models were computationally efficient and ready for mobile
devices or devices of low computational power. These advances empower the
development of a resource-efficient and high performing AI-based metastatic
breast cancer diagnostic system that can adapt to under-resourced healthcare
facilities in developing countries. This research provides an innovative
technological solution to address the long delays in metastatic breast cancer
diagnosis and the consequent disparity in patient survival outcome in
developing countries.

为解决发展中国家乳腺癌诊断的延误和患者生存率的不平等问题，本研究开发了一种基于深度学习的转移性乳腺癌诊断系统，具有高诊断准确性和计算效率，能够适应资源匮乏的医疗设施。

为发展中国家减少癌症诊断的长时间延误，提高患者生存率，设计一种基于深度学习的资源高效的诊断系统

Designing a Deep Learning-Driven Resource-Efficient Diagnostic System  for Metastatic Breast Cancer: Reducing Long Delays of Clinical Diagnosis and  Improving Patient Survival in Developing Countries

The International Symposium on Biomedical Imaging (ISBI) held a grand
challenge to evaluate computational systems for the automated detection of
metastatic breast cancer in whole slide images of sentinel lymph node biopsies.
Our team won both competitions in the grand challenge, obtaining an area under
the receiver operating curve (AUC) of 0.925 for the task of whole slide image
classification and a score of 0.7051 for the tumor localization task. A
pathologist independently reviewed the same images, obtaining a whole slide
image classification AUC of 0.966 and a tumor localization score of 0.733.
Combining our deep learning system's predictions with the human pathologist's
diagnoses increased the pathologist's AUC to 0.995, representing an
approximately 85 percent reduction in human error rate. These results
demonstrate the power of using deep learning to produce significant
improvements in the accuracy of pathological diagnoses.

使用深度学习技术，结合人工病理学家的诊断结果，实现对淋巴结活检全滑片图像中转移性乳腺癌的自动检测，能够在提高病理诊断准确性方面发挥重要作用。