Deep learning for bioimage analysis

Abstract

Deep learning has transformed the way large and complex image datasets can be processed, reshaping what is possible in bioimage analysis. As the complexity and size of bioimage data continues to grow, this new analysis paradigm is becoming increasingly ubiquitous. In this Review, we begin by introducing the concepts needed for beginners to understand deep learning. We then review how deep learning has impacted bioimage analysis and explore the open-source resources available to integrate it into a research project. Finally, we discuss the future of deep learning applied to cell and developmental biology. We analyse how state-of-the-art methodologies have the potential to transform our understanding of biological systems through new image-based analysis and modelling that integrate multimodal inputs in space and time.

arXiv comment28 pages with 2 figures, 4 boxes and 1 table - Updated version (V2.0)

References in corpus (9)

• A Survey on Deep Learning in Medical Image Analysis
• The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks
• Sanity Checks for Saliency Maps
• This Looks Like That: Deep Learning for Interpretable Image Recognition
• Learning to Control Self-Assembling Morphologies: A Study of Generalization via Modularity
• A joint model of unpaired data from scRNA-seq and spatial transcriptomics for imputing missing gene expression measurements
• The Mutex Watershed and its Objective: Efficient, Parameter-Free Graph Partitioning
• Image Inversion and Uncertainty Quantification for Constitutive Laws of Pattern Formation
• KuraNet: Systems of Coupled Oscillators that Learn to Synchronize