We hope this contributes to open & reproducible research in #ComputationalPathology.
Big thanks to Guillaume Balezo, Albert Pla Planas and Etienne Decencière.
Great collaboration between @sanofifr.bsky.social and @minesparis-psl.bsky.social.
#DigitalPathology #FoundationModels #ViT #HuggingFace
🧪 Try it yourself!
We've released an interactive demo on Hugging Face 🤖
👉 huggingface.co/spaces/Estab...
👨💻 Code: github.com/Sanofi-Publi...
💡 The core idea of MIPHEI-ViT was to use a ViT foundation encoder (H-optimus-0) for this dense prediction task, thus leveraging the power of pathology foundation models for cross-modality prediction.
🔬 H&E stained tissue slides are cheap, and available for huge retrospective cohorts. mIF (and in particular Orion) are much more informative on particular cell types, but not available for large-scale cohorts.
💡 MIPHEI-ViT bridges the gap, learning to predict mIF from H&E.
🚨 New preprint + open-source release!
We're excited to share MIPHEI-ViT — a model that predicts multiplex immunofluorescence (mIF) from H&E-stained histology images using Vision Transformer (ViT) foundation models.
📄
So honored to have welcomed @janefonda.com to Institut Curie in Paris!
After receiving the 2024 Marie Curie Legacy Award last November, actress and activist Jane Fonda wanted to meet with our teams for an immersive look at the institute’s cutting-edge scientific and medical work. 💡🧬
#PRAIRIE
@minesparis-psl.bsky.social
@institutcurie.bsky.social
@inserm.fr
🔬 We’re also releasing a massive new dataset — over 600,000 annotated nucleus images — now freely available on the BioImage Archive.
www.ebi.ac.uk/biostudies/b...
🧠 We introduce Cell-Cycle Variational Auto-Encoders (CC-VAE) — a deep learning framework to robustly and consistently predict cell cycle phase from microscopy images.
🎉 We're excited to share our latest work: "A Deep Learning approach for time-consistent cell cycle phase prediction from microscopy data", now available on bioRxiv!
www.biorxiv.org/content/10.1...
If you are interested in spatial transcriptomics and want to get down to single-cell level, then this is for you! 3/3
@minesparis-psl.bsky.social
@institutcurie.bsky.social
@inserm.fr
#PRAIRIE
Huge thanks to Lucie Gaspard-Boulinc and Luca Gortana for this amazing work and Florence Cavalli and Emmanuel Barillot for this wonderful collaboration at the U1331 - Computational Oncology 2/3
Excited to share our review in Nature Reviews Genetics on cell-type deconvolution!
"Cell-type deconvolution methods for spatial transcriptomics"
🔗 nature.com/articles/s41...
📖 Free access: rdcu.be/el0Ka
We have open Postdoc / PhD student positions in computational pathology and agentic AI, based in 🇩🇪🇪🇺 Ideally with a CS background, but we do 🧡 biologists with self-taught Python and ML skills
🌼 Faites fleurir l'espoir contre le #cancer avec l'Institut Curie !
Tout au long du mois de mars, participez à la campagne #UneJonquilleContreleCancer et devenez acteurs de la lutte contre le #cancer aux côtés de nos équipes 🤝
👉 unejonquillecontrelecancer.fr
🔗 Resources
- Preprint: doi.org/10.1101/2025...
- RNA2seg package: github.com/fish-quant/r...
- Annotated datasets: zenodo.org/records/1491...
Looking forward to feedback from the community!
Accurate cell segmentation is critical in spatial transcriptomics but often challenged by poor staining and complex tissues. RNA2seg addresses this by integrating all available data types—membrane, nuclear staining, and RNA positions.
RNA2Seg is a deep learning model trained on over 4 million cells across 7 organs, integrating RNA point clouds and multiple stainings for robust and accurate cell segmentation in image based spatial transcriptomics.
🎉 Happy to share our new preprint!
Congratulations to Thomas Defard and Alice Blondel for their outstanding work on RNA2seg – a generalist model for cell segmentation in image-based spatial transcriptomics.
The work was supervised by Florian Muller (Pasteur) and myself.
