This tutorial will focus on emerging topics in deep learning-based microscopy image analysis. Recently, deep learning methodology for microscopy image analysis has evolved from application and optimisation of existing neural networks to more sophisticated models that are designed to target challenges in this problem domain more specifically and effectively. For instance, non-fully supervised learning approaches have recently attracted much attention to address the difficulty of limited training data; neural architecture search has been developed to enable automatic network optimisation; knowledge enhanced learning is another mechanism to enhance model performance without relying on excessive ground truth annotations; and many approaches have been developed to improve the robustness and interpretability of deep learning models. In this tutorial, we will present a broad and comprehensive overview of these various topics and engage researchers to discuss future research directions.

The tutorial aims to present a broad overview of several important emerging trends in deep learning for microscopy image analysis, providing a venue to summarise the current state-of-the-art as well as identifying promising future directions. Specifically, the tutorial will help the audience achieve the following goals:

• Gain general knowledge of non-fully supervised learning in microscopy image analysis and gain familiarity with the main methods used in current state-of-the-art models;

• Understand the main strategies of neural architecture search and analyse several recent studies in this domain;

• Build a clear understanding of various types of knowledge enhanced learning and evaluate the advantages and disadvantages of different approaches for different problems; and,

• Analyse issues of data imbalance, heterogeneity and interpretability and obtain an understanding of various techniques addressing these issues.

Yang Song, University of New South Wales, Australia

Maurice Pagnucco, University of New South Wales, Australia

Erik Meijering, University of New South Wales, Australia

Weidong Cai, University of Sydney, Australia

Jianhua Yao, Tencent, China

Mei Chen, Microsoft, USA