Deep Weak Learning for Morphology Analysis of Micro and Nanoscale Images

该项目将开发新的方法，用于从微米和纳米级图像中自动发现和量化图像表型。其结果将是生物医学图像分析领域的最新进展，特别关注用于形态特征表示的广义弱监督深度学习模型。这些方法将为具有高度异质性和有限训练数据的真实的生物医学成像场景转变深度学习管道。这些框架将促进广泛的显微图像模式和生物应用的高通量处理，并有可能成为下一代计算平台，以支持人类生物学的基础研究。

This project will develop novel methods for automated discovery and quantification of image phenotypes from micro and nanoscale images. The outcome will be an advance of the state of the art in biomedical image analysis with a particular focus on generalized weakly-supervised deep learning models for morphological feature representation. The methodologies will transform the deep learning pipeline for real biomedical imaging scenarios with high heterogeneity and limited training data. The frameworks will facilitate high-throughput processing for a wide range of microscopy image modalities and biological applications, and potentially become the next generation computational platform to support fundamental research in human biology.