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

In this paper we survey, analyze and criticize current art on automated
melanoma screening, reimplementing a baseline technique, and proposing two
novel ones. Melanoma, although highly curable when detected early, ends as one
of the most dangerous types of cancer, due to delayed diagnosis and treatment.
Its incidence is soaring, much faster than the number of trained professionals
able to diagnose it. Automated screening appears as an alternative to make the
most of those professionals, focusing their time on the patients at risk while
safely discharging the other patients. However, the potential of automated
melanoma diagnosis is currently unfulfilled, due to the emphasis of current
literature on outdated computer vision models. Even more problematic is the
irreproducibility of current art. We show how streamlined pipelines based upon
current Computer Vision outperform conventional models - a model based on an
advanced bags of words reaches an AUC of 84.6%, and a model based on deep
neural networks reaches 89.3%, while the baseline (a classical bag of words)
stays at 81.2%. We also initiate a dialog to improve reproducibility in our
community

本文针对自动黑素瘤筛查的研究现状进行了调查、分析和批评，并重新实施了一个基线技术并提出了两个新的技术。研究表明，基于当前的计算机视觉模型的简化流程比传统模型的性能更好，基于高级词袋的模型的 AUC 为 84.6％，基于深度神经网络的模型达到 89.3％，而基线（经典词袋）为 81.2％。我们还开始对我们的社区进行改进重复性的对话。