Alexandre Filiot

Foundation models for pathology @ Waiv

Preprints

  1. Filiot, A., Thaeter, O., Schmauch, B., & Guillou, L. (2026). Robustifying Pathology Foundation Models via Fine-Tuning. In arXiv preprint arXiv:2607.22861.
    arXiv model blog post
  2. Duboudin, T., Fontaine, X., Andrier, E., Guillou, L., Filiot, A., Baiocco-Rodrigues, T., Olivier, A., Romagnoni, A., Klein, J., & Schiratti, J.-B. (2026). CytoSyn: a Foundation Diffusion Model for Histopathology. In arXiv preprint arXiv:2603.18089.
    arXiv
  3. Gaury, V., Vaquette, Z., Aubert, V., Barcenas, C., Jones, L. J., Jacobs, D., Guillon, J., Filiot, A., van der Vegt, B., Hocquet, E., Maisin, C., Spary, L. K., Bateson, M., Martin, A.-L., Drubay, D., & Everhard, S. (2025). Clinical Validation of RlapsRisk BC in an International Multi-Cohorts Setting. In medRxiv.
    paper
  4. Filiot, A., Jacob, P., Mac Kain, A., & Saillard, C. (2024). Phikon-v2, A Large and Public Feature Extractor for Biomarker Prediction. In arXiv preprint arXiv:2409.09173.
    arXiv model
  5. Filiot, A., Ghermi, R., Olivier, A., Jacob, P., Fidon, L., Kain, A. M., Saillard, C., & Schiratti, J.-B. (2023). Scaling Self-Supervised Learning for Histopathology with Masked Image Modeling. In medRxiv. Cold Spring Harbor Laboratory Press.
    paper

Journal articles

  1. Schmauch, B., Herpin, L., Olivier, A., Duboudin, T., Dubois, R., Gillet, L., Filiot, A., Schiratti, J.-B., Di Proietto, V., Le Corre, D., Bourgoin, A., Taïeb, J., Emile, J.-F., Fridman, W. H., & Pronier, E. (2025). A Deep Learning-Based Multiscale Integration of Spatial Omics with Tumor Morphology. Nature Communications, 16.
    paper
  2. Garberis, I., Gaury, V., Saillard, C., Drubay, D., Elgui, K., Schmauch, B., Jaeger, A., Herpin, L., Linhart, J., Sapateiro, M., Bernigole, F., Kamoun, A., Filiot, A., Tchita, O., Dubois, R., Auffret, M., Guillou, L., Bousaid, I., Azoulay, M., … Everhard, S. (2025). Deep Learning Assessment of Metastatic Relapse Risk from Digitized Breast Cancer Histological Slides. Nature Communications, 16.
    paper
  3. Oubenali, N., Messaoud, S., Filiot, A., Lamer, A., & Andrey, P. (2022). Visualization of Medical Concepts Represented using Word Embeddings: a Scoping Review. BMC Medical Informatics and Decision Making, 22(1), 83.
    paper
  4. Guardiolle, V., Bazoge, A., Morin, E., Daille, B., Toublant, D., Bouzille, G., Merel, Y., Pierre-Jean, M., Filiot, A., Cuggia, M., Wargny, M., Lamer, A., & Gourraud, P. A. (2022). Large-scale Matching Algorithm for Linking Biomedical Data Warehouse Records with the National Mortality Database in France. JMIR Medical Informatics, 10(11).
    paper
  5. Coisne, A., Montaigne, D., Ninni, S., Lamblin, N., Lemesle, G., Delsart, P., Filiot, A., Andrey, P., Balaye, P., Butruille, L., Decoin, R., Woitrain, E., Granada, J. F., Staels, B., & Bauters, C. (2022). Diabetes mellitus and cardiovascular mortality across the spectrum of aortic stenosis. Heart.
    paper
  6. Chepy, A., Vivier, S., Bray, F., Ternynck, C., Meneboo, J.-P., Figeac, M., Filiot, A., Guilbert, L., Jendoubi, M., Rolando, C., Launay, D., Dubucquoi, S., Marot, G., & Sobanski, V. (2022). Effects of immunoglobulins G from systemic sclerosis patients in normal dermal fibroblasts: A multi-omics study. Front. Immunol., 13, 904631.
  7. Courtois, M., Filiot, A., & Ficheur, G. (2021). Distribution-Based Similarity Measures Applied to Laboratory Results Matching. Studies in Health Technology and Informatics, 287, 94–98.
    paper

Conference papers

  1. Filiot, A., Dop, N., Tchita, O., Riou, A., Dubois, R., Peeters, T., Valter, D., Scalbert, M., Saillard, C., Robin, G., & Olivier, A. (2025). Distilling Foundation Models for Robust and Efficient Models in Digital Pathology. Medical Image Computing and Computer Assisted Intervention (MICCAI 2025), 162–172.
    paper arXiv model
  2. Lubrano, M., Bigaud, N., Baiocco-Rodrigues, T., Brulport, F., Filiot, A., & Lin, D. (2024, July). Improving Model Generalization to Out-of-Domain Data in Histopathology: Leveraging Simple Techniques for External Validation. Medical Imaging with Deep Learning (MIDL 2024), Short Papers.
    paper
  3. José-García, A., Jacques, J., Filiot, A., Handl, J., Launay, D., Sobanski, V., & Dhaenens, C. (2022). Multi-view Clustering of Heterogeneous Health Data: Application to Systemic Sclerosis. In G. Rudolph, A. V. Kononova, H. Aguirre, P. Kerschke, G. Ochoa, & T. Tušar (Eds.), Parallel Problem Solving from Nature – PPSN XVII (pp. 352–367). Springer International Publishing.
  4. Lamer, A., Filiot, A., Bouillard, Y., Mangold, P., Andrey, P., & Schiro, J. (2021, May). Specifications for the Routine Implementation of Federated Learning in Hospitals Networks. Studies in Health Technology and Informatics, Volume 281: Public Health and Informatics.
    paper
  5. Mangold, P., Filiot, A., Moussa, M., Sobanski, V., Ficheur, G., Andrey, P., & Lamer, A. (2020). A Decentralized Framework for Biostatistics and Privacy Concerns. EFMI Special Topic Conference, Integrated Citizen Centered Digital Health and Social Care(275), 137–141.
    paper code

Conference abstracts and posters

  1. Samuelsson, E., Le Douget, J.-E., Filiot, A., Guillon, J., Schmauch, B., Lin, D., Maisin, C., Vianey, K., Farag, M., Wiscart, C., Deslandes, H., Vaquette, Z., & von Loga, K. (2026). Towards Tumor-Agnostic IHC Cell Detection and Classification. European Congress on Digital Pathology (ECDP 2026), poster P45.
    poster
  2. Lubrano, M., Samuelsson, E., Filiot, A., Jacobs, D., Lyon, A., Wiscart, C., Genestie, C., Bani, M.-A., Guettier, C., Svrcek, M., Wang, T., Morrison, J., Hernández-Losa, J., Broeckx, G., Deman, F., & von Loga, K. (2026). Beyond CRC: AI-Powered MSI Status Detection Across Gastric, Endometrial and Biliary Tract Cancers — Robust Validation Across 11 International Cohorts and Novel Cross-Indication Insights. Laboratory Investigation, 106, 104785.Abstract 500, USCAP 115th Annual Meeting
    paper poster
  3. Filiot, A., Samuelsson, E., Schmauch, B., Olivier, A., Guillon, J., Lin, D., Le Douget, J.-E., Guillou, L., Lacroix-Triki, M., Maisin, C., Vianey, K., Kendall, T. J., McHayle, A., Deslandes, H., Vaquette, Z., Farag, M. S., & Von Loga, K. (2025). A Lightweight IHC-Specific Foundation Model for Diagnostic Purposes. ESMO Real World Data and Digital Oncology, 10, 100232.Abstract 33P, ESMO AI & Digital Oncology Congress
    paper poster
  4. Le Douget, J.-E., Schmauch, B., Filiot, A., Guillon, J., Lin, D., Rodriguez, Y., Olivier, A., Guillou, L., Deslandes, H., Maisin, C., Vianey, K., Kendall, T. J., McHayle, A., Lacroix-Triki, M., Vaquette, Z., Farag, M. S., & Von Loga, K. (2025). A Cell-Level Staining Quantification Framework for HER2 Prediction. ESMO Real World Data and Digital Oncology, 10, 100207.Abstract 8P, ESMO AI & Digital Oncology Congress
    paper poster
  5. Lubrano, M., Samuelsson, E., Filiot, A., Dubois, R., Klein, J., Jacobs, D., Lyon, A., Van Praet, L., Wiscart, C., Fouillet, A., Genestie, C., Wang, T., Morrison, J., Hernandez-Losa, J., Deman, F., Fléjou, J.-F., Kats-Ugurlu, G., Kammerer-Jacquet, S.-F., O’Sullivan, B., & von Loga, K. (2025). Towards Achieving Full MSI Reflex Testing in CRC, GC and EC: An Efficient and Streamlined Approach Using Deep Learning. Annals of Oncology, 36, S961–S962.Abstract 1756eP, ESMO Congress
    paper poster

Software and models

  1. Filiot, A. (2026). Histoboard: A Unified Dashboard for Pathology Foundation Model Benchmarks. Open-source dashboard aggregating 12 benchmarks, 48 models and 400+ evaluation tasks.
    code demo blog post
  2. Filiot, A., Thaeter, O., Schmauch, B., & Guillou, L. (2026). Phaet and Mascaret: Robustness Fine-Tuned Pathology Foundation Models. Model weights on Hugging Face (robust variants of Phikon-v2 and Midnight-12k).
    arXiv model
  3. Filiot, A., Dop, N., Tchita, O., Riou, A., Dubois, R., Peeters, T., Valter, D., Scalbert, M., Saillard, C., Robin, G., & Olivier, A. (2025). H0-mini: A Distilled Pathology Foundation Model. Model weights on Hugging Face.
    arXiv model
  4. Filiot, A., Jacob, P., Mac Kain, A., & Saillard, C. (2024). Phikon-v2: A Public ViT-L Feature Extractor for Histopathology. Model weights on Hugging Face.
    arXiv model
  5. Filiot, A., Ghermi, R., Olivier, A., Jacob, P., Fidon, L., Mac Kain, A., Saillard, C., & Schiratti, J.-B. (2023). Phikon and HistoSSLscaling: Self-Supervised Pre-Training for Histopathology. Reference implementation and model weights on Hugging Face.
    code model

Blog posts

  1. Filiot, A. (2026). Meet Phaet and Mascaret: Two Pathology Foundation Models Built to Generalize Across Labs. Waiv blog.
    paper
  2. Filiot, A. (2026). Meet Histoboard: Your Interactive Map for Navigating Foundation Models for Pathology. Waiv blog.
    paper
  3. Imran, A., Filiot, A., Cummings, H., & Perochon, T. (2023). Scaling Self-Supervised Learning for Histology: Introducing Phikon. Hugging Face community blog.
    paper

In the media

  1. Nature Research Custom Media. (2025). AI in Digital Pathology: Smaller Models, Bigger Impact. Nature advertisement feature (interview).
    paper

Peer review

  • Nature Communications — reviewer, 2026

Verified reviews are deposited on ORCID 0000-0001-7860-2245.