Work

(Giga)TIME for the era of AI: virtual multiplex immunofluorescence from routine H&E for large populations

Year: 2026

Type: article

Abstract: In a recent paper published in Cell , Valanarasu, Xu, Usuyama, Kim et al. introduced GigaTIME, a multimodal artificial intelligence (AI) framework that generates photorealistic virtual multiplex immunofluorescence (mIF) images from routine hematoxylin and eosin (H&E)-stained pathology slides. 1 The study proposes that H&E-stained images, paired with large-scale multimodal AI training, can serve as a hypothesis-generating route to population-scale modeling of marker-like virtual mIF images that approximate the tumor immune microenvironment (TIME), enabling spatially informed association screening across real-world patient cohorts that would be impractical to profile with experimental multiplex imaging at comparable scale.

Source: Signal Transduction and Targeted Therapy

DOI: 10.1038/s41392-026-02643-2

Authors: Shao-Ping Yang, Dihua Yu

Institutions: The University of Texas MD Anderson Cancer Center, The University of Texas Health Science Center at Houston

Language: English

Topic: AI in cancer detection

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $3,790