Francesco Ciompi

I am a scientist with more than 15 years of experience in medical image analysis and computer vision, working at the intersection of pathology, oncology, and artificial intelligence. As an Associate Professor of computational pathology, I lead a multidisciplinary team in the pathology department of the Radboud University Medical Center, working on precision oncology, developing artificial intelligence models to better understand diseases, discover novel biomarkers, and help physicians to improve their routine diagnostics and to select the right treatments to help patients.
New publications
[24/04/2024] Esther M.M. Smeets, Marija Trajkovic-Arsic, Daan Geijs, Sinan Karakaya, Monica van Zanten, Lodewijk A.A. Brosens, Benedikt Feuerecker, Martin Gotthardt, Jens T. Siveke, Rickmer Braren, Francesco Ciompi and Erik H.J.G. Aarntzen, "Histology-based radiomics for [18F]FDG-PET identifies tissue heterogeneity in pancreatic cancer", Journal of Nuclear Medicine, 2024 [accepted].

[05/02/2024] Nohemi S. Leon Contreras, Clément Grisi, Witali Aswolinskiy, Simona Vatrano, Filippo Fraggetta, Iris Nagtegaal, Marina D’Amato and Francesco Ciompi, "Benchmarking hierarchical image pyramid transformer for the classification of colon biopsies and polyps histopathology images", IEEE International Symposium on Biomedical Imaging (ISBI), 2024 [accepted].

[05/02/2024] Cristian Tommasino, Cristiano Russo, Antonio Maria Rinaldi and Francesco Ciompi, "HoVer-UNet: Accelerating HoVerNet with UNet-based multi-class nuclei segmentation via knowledge distillation", IEEE International Symposium on Biomedical Imaging (ISBI), 2024 [accepted].

[05/02/2024] Carlijn M. Lems, Daan J. Geijs, John-Melle Bokhorst, Maxime Sülter, Leander van Eekelen and Francesco Ciompi, "Color deconvolution for color-agnostic and cross-modality analysis of immunohistochemistry whole-slide images with deep learning", IEEE International Symposium on Biomedical Imaging (ISBI), 2024 [accepted].

NEWS

COMPAYL 24 workshop at MICCAI

Computational Pathology @ MICCAI is back! After successful past editions in 2018, 2019 and 2021, consolidating a space for computational pathology at MICCAI, this year, we are back with COMPAYL, for computational pathology with multimodal data.

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NWO OTP grant to find lung cancer-specific treatment

AI to analyse cultures of lung cancer and pulmonary fibrosis tissue via higher harmonic generation (HHG) 3D microscopy images over time (4D data), to find the best tumor-specific treatment among a set of tested drugs within a few days.

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ECDP 2024

Three scientific works accepted at the European Congress on Digital Pathology 2024

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francescociompi.com | 2024