Image processing articles from across Nature Portfolio

Image processing is manipulation of an image that has been digitised and uploaded into a computer. Software programs modify the image to make it more useful, and can for example be used to enable image recognition.

Latest Research and Reviews

Automated Association for Osteosynthesis Foundation and Orthopedic Trauma Association classification of pelvic fractures on pelvic radiographs using deep learning

• Seung Hwan Lee
• Jisu Jeon
• Kwang Gi Kim

A pathology foundation model for cancer diagnosis and prognosis prediction

A study describes the development of a generalizable foundation machine learning framework to extract pathology imaging features for cancer diagnosis and prognosis prediction.

• Xiyue Wang
• Junhan Zhao
• Kun-Hsing Yu

A multi-task deep learning approach for real-time view classification and quality assessment of echocardiographic images

• Xinyu Li
• Hongmei Zhang
• Shenghua Xie

Enhancing representation in radiography-reports foundation model: a granular alignment algorithm using masked contrastive learning

Multi-modal foundation models are increasingly important in medical applications. Here, authors show a masked contrastive chest X-ray model that achieves fine-grained image understanding and zero-shot capabilities, outperforming existing methods

• Weijian Huang
• Cheng Li
• Shanshan Wang

Impact of acquisition area on deep-learning-based glaucoma detection in different plexuses in OCTA

• Julia Schottenhamml
• Tobias Würfl
• Christian Mardin

A differential network with multiple gated reverse attention for medical image segmentation

• Shun Yan
• Benquan Yang
• Aihua Chen

News and Comment

Cell Painting Gallery: an open resource for image-based profiling

• Erin Weisbart
• Ankur Kumar
• Shantanu Singh

The promise of machine learning approaches to capture cellular senescence heterogeneity

The identification of senescent cells is a long-standing unresolved challenge, owing to their intrinsic heterogeneity and the lack of universal markers. In this Comment, we discuss the recent advent of machine-learning-based approaches to identifying senescent cells by using unbiased, multiparameter morphological assessments, and how these tools can assist future senescence research.

• Imanol Duran
• Cleo L. Bishop
• Ryan Wallis

Visual interpretability of bioimaging deep learning models

The success of deep learning in analyzing bioimages comes at the expense of biologically meaningful interpretations. We review the state of the art of explainable artificial intelligence (XAI) in bioimaging and discuss its potential in hypothesis generation and data-driven discovery.

• Oded Rotem
• Assaf Zaritsky

Next-generation AI for connectomics

New approaches in artificial intelligence (AI), such as foundation models and synthetic data, are having a substantial impact on many areas of applied computer science. Here we discuss the potential to apply these developments to the computational challenges associated with producing synapse-resolution maps of nervous systems, an area in which major ambitions are currently bottlenecked by AI performance.

• Michał Januszewski
• Viren Jain

Multimodal large language models for bioimage analysis

Multimodal large language models have been recognized as a historical milestone in the field of artificial intelligence and have demonstrated revolutionary potentials not only in commercial applications, but also for many scientific fields. Here we give a brief overview of multimodal large language models through the lens of bioimage analysis and discuss how we could build these models as a community to facilitate biology research.

• Shanghang Zhang
• Gaole Dai
• Jianxu Chen

Neurotransmitters at a glance

Machine learning approaches can distinguish six different classes of presynapses from electron micrographs across the Drosophila brain.

• Rita Strack