Leveraging Deep Learning in Biological Image Analysis

Abstract

Biological image analysis is crucial for advancements in fields like drug discovery and biomedical research. Traditional methods, relying on manual feature extraction, are laborious and prone to errors. While deep learning has revolutionized automated image analysis, many challenges remain. These include the need for vast amounts of high-quality labeled data for training, limited generalizability to unseen data, and the computational complexity requiring expensive hardware. This project investigates the application of deep learning for biological image analysis, focusing on overcoming these limitations in three key areas: large image representation, accurate cell segmentation, and spatial image analysis. By addressing these challenges, we aim to develop robust and accessible deep learning tools that can significantly enhance biological image analysis, ultimately accelerating research progress.