GeMM-GAN: A Multimodal Generative Model Conditioned on Histopathology Images and Clinical Descriptions for Gene Expression Profile Generation.
- DOI
- 10.1007/978-3-032-11317-7_33
- Published
- 2026
- Container
- Lecture Notes in Computer Science
- Publisher
- Springer Nature Switzerland
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1007/978-3-032-11317-7_33,
title = {GeMM-GAN: A Multimodal Generative Model Conditioned on Histopathology Images and Clinical Descriptions for Gene Expression Profile Generation.},
author = {Francesca Pia Panaccione and Carlo Sgaravatti and Pietro Pinoli},
year = {2026},
journal = {Lecture Notes in Computer Science},
doi = {10.1007/978-3-032-11317-7_33},
url = {https://doi.org/10.1007/978-3-032-11317-7_33}
}RIS
TY - JOUR TI - GeMM-GAN: A Multimodal Generative Model Conditioned on Histopathology Images and Clinical Descriptions for Gene Expression Profile Generation. AU - Francesca Pia Panaccione AU - Carlo Sgaravatti AU - Pietro Pinoli PY - 2026 JO - Lecture Notes in Computer Science DO - 10.1007/978-3-032-11317-7_33 UR - https://doi.org/10.1007/978-3-032-11317-7_33 ER -
APA
Panaccione, F. P., Sgaravatti, C., & Pinoli, P. (2026). GeMM-GAN: A Multimodal Generative Model Conditioned on Histopathology Images and Clinical Descriptions for Gene Expression Profile Generation.. Lecture Notes in Computer Science. https://doi.org/10.1007/978-3-032-11317-7_33
Source records
- crossref · retrieved 2026-09-25T00:39:19.766Z