Applications of Artificial Intelligence, Deep Learning, and Machine Learning to Support the Analysis of Microscopic Images of Cells and Tissues

2025/02/15 by Muhammad Ali, Viviana Benfante, Ghazal Basirinia +9 · 1 voice
Biochemistry, Genetics and Molecular Biology · Computer Science · #Cell Image Analysis Techniques #AI in cancer detection #Digital Imaging for Blood Diseases

paper · doi:10.3390/jimaging11020059

openalex publication_date 2025/02/15 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/01

Abstract

Artificial intelligence (AI) transforms image data analysis across many biomedical fields, such as cell biology, radiology, pathology, cancer biology, and immunology, with object detection, image feature extraction, classification, and segmentation applications. Advancements in deep learning (DL) research have been a critical factor in advancing computer techniques for biomedical image analysis and data mining. A significant improvement in the accuracy of cell detection and segmentation algorithms has been achieved as a result of the emergence of open-source software and innovative deep neural network architectures. Automated cell segmentation now enables the extraction of quantifiable cellular and spatial features from microscope images of cells and tissues, providing critical insights into cellular organization in various diseases. This review aims to examine the latest AI and DL techniques for cell analysis and data mining in microscopy images, aid the biologists who have less background knowledge in AI and machine learning (ML), and incorporate the ML models into microscopy focus images.

Citations

• Process analytical technology (PAT) for biopharmaceutical products: Part II. Concepts and applications
• Review of deep learning: concepts, CNN architectures, challenges, applications, future directions
• Efficient extraction of deep image features using convolutional neural network (CNN) for applications in detecting and analysing complex food matrices
• Generative Adversarial Nets
• CellViT: Vision Transformers for Precise Cell Segmentation and Classification
• All you need is data preparation: A systematic review of image harmonization techniques in Multi-center/device studies for medical support systems
• Hyperspectral imaging and its applications: A review

Discussions

• Applications of Artificial Intelligence, Deep Learning, and Machine Learning to Support the Analysis of Microscopic Images of Cells and Tissues. J. Imaging 2025, 11(2), 59; doi.org/10.3390/jima... # [bsky, 0 points, 0 comments]