Developing interpretable machine learning models for clinical imaging and single-cell genomics

机器学习方法对于充分利用生物医学科学中高通量技术产生的大量数据至关重要。然而，为了充分利用这些模型，我们需要能够打开“黑匣子”。我将使用精选的临床和公共研究数据来基准测试和开发可解释的深度学习模型和软件工具。这些模型将用于乳腺癌筛查计划和复杂的大规模单细胞基因组学数据分析。

Machine learning methods will be vital to make best use of the deluge of data generated by high-throughput technologies in biomedical science. To get the most out of these models, however, we need to be able to unpack the 'black box'. I will use curated clinical and public research data to benchmark and develop interpretable deep learning models and software tools. These models will be used for breast cancer screening programs and for analysis of complex, large-scale single-cell genomics data.