arXiv 2102.019792021-02-03cs.LGcs.CY

在临床放射学实践中开发和部署机器学习模型的关键技术考虑

Key Technology Considerations in Developing and Deploying Machine Learning Models in Clinical Radiology Practice

DeepTek Inc（DeepTek公司）

Viraj Kulkarni, Manish Gawali, Amit Kharat

摘要：

近年来，使用机器学习来开发用于解释放射学图像的智能软件工具已经获得了广泛的关注。然而，这些模型在临床实践中的开发、部署和最终采用仍然充满挑战。在本文中，我们提出了一系列机器学习研究人员必须认识和解决的关键考虑因素，以使他们的模型准确，鲁棒，并在实践中可用。也就是说，我们讨论：训练数据不足，分散的数据集，注释的高成本，模糊的基础事实，类表示的不平衡，不对称的误分类成本，相关的性能指标，模型对看不见的数据集的泛化，模型衰减，对抗攻击，可解释性，公平性和偏见，以及临床验证。我们描述了每一个考虑因素，并确定技术来解决它。虽然这些技术已经在以前的研究文献中讨论过，通过在医学成像的背景下重新检查它们，并以洗衣清单的形式编译它们，我们希望使它们更容易被研究人员，软件开发人员，放射科医生和其他利益相关者使用。

英文摘要：

The use of machine learning to develop intelligent software tools for interpretation of radiology images has gained widespread attention in recent years. The development, deployment, and eventual adoption of these models in clinical practice, however, remains fraught with challenges. In this paper, we propose a list of key considerations that machine learning researchers must recognize and address to make their models accurate, robust, and usable in practice. Namely, we discuss: insufficient training data, decentralized datasets, high cost of annotations, ambiguous ground truth, imbalance in class representation, asymmetric misclassification costs, relevant performance metrics, generalization of models to unseen datasets, model decay, adversarial attacks, explainability, fairness and bias, and clinical validation. We describe each consideration and identify techniques to address it. Although these techniques have been discussed in prior research literature, by freshly examining them in the context of medical imaging and compiling them in the form of a laundry list, we hope to make them more accessible to researchers, software developers, radiologists, and other stakeholders.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

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