FRANCESCO CIOMPI

francescociompi.com | 2025

EXAMODE

Background

Big volumes of diverse data from distributed sources are continuously produced. Supervised deep learning models, require large amount of annotated data. Considering the available data, such annotations are impractical. The ExaMode project was a collaboration between universities and industries and addressed this impracticality by applying weakly supervised deep learning based on diverse data from distributed sources.

Aim

The project aimed to solve: "weakly supervised knowledge discovery of exascale heterogeneous data.’’

Radboudumc was responsible for the following objectives:

  • Develop deep learning methods for detection and semantic segmentation of regions of interest in histopathology whole-slide images.
  • Develop deep learning methods for whole-slide image classification using image-level target labels extracted from pathology reports.
  • Develop methods of whole-slide image compression to allow end-to-end training of deep neural networks with whole-slide image data.
  • Develop efficient parallel implementations of researched ANNs dealing with both shared and distributed memory environments.
  • Make developed methods available via a web-based platform.
  • Facilitate integration of developed methods in digital pathology workflow and development of decision support algorithms.

Publications

  • M. van Rijthoven, W. Aswolinskiy, L. Tessier, M. Balkenhol, J. Bogaerts, D. Drubay, L. Blesa, D. Peeters, E. Stovgaard, A. L\aenkholm , H. Haynes, L. Craciun, D. Larsimont, M. Amgad, L. Cooper, C. de Kock, V. Dechering, J. Lotz, N. Weiss, M. van Bockstal, C. Galant, E. Lips, H. Horlings, J. Wesseling, L. Mulder, S. van den Belt, K. Weber, P. Jank, C. Denkert, E. Munari, G. Bogina, C. Russ, A. Lemm, S. Loi, J. Douglas, S. Michiels, H. Joensuu, M. Fan, D. Lee, J. Ye, K. Byun, J. Kim, S. Xu, Z. Ji, F. Xie, J. Kuang, X. Chen, L. Chen, A. Tsakiroglou, R. Byers, M. Fergie, V. Ramanathan, A. Martel, A. Shephard, S. Ahmed Raza, M. Jahanifar, N. Rajpoot, S. Cho, D. Kim, H. Jang, C. Park, K. Kim, R. Donders, S. Maurits, M. Groeneveld, A. Mickan, J. Meakin, B. van Ginneken, R. Salgado, J. van der Laak and F. Ciompi, "Tumor-infiltrating lymphocytes in breast cancer through artificial intelligence: biomarker analysis from the results of the TIGER challenge", https://doi.org/10.1101/2025.02.28.25323078, 2025.
  • Witali Aswolinskiy, Rachel S van der Post, Michiel Simons, Enrico Munari, Michela Campora, Carla Baronchelli, Laura Ardighieri, Simona Vatrano, Iris Nagtegaal, Jeroen van der Laak, Francesco Ciompi, "Attention-based whole-slide image compression achieves pathologist-level pre-screening of multi-organ routine histopathology biopsies", https://doi.org/10.1101/2024.12.17.24319180, 2025.
  • D. Höppener, W. Aswolinskiy, Z. Qian, D. Tellez, P. Nierop, M. Starmans, I. Nagtegaal, M. Doukas, J. de Wilt, D. Grünhagen, J. van der Laak, P. Vermeulen, F. Ciompi and C. Verhoef, "Classifying histopathological growth patterns for resected colorectal liver metastasis with a deep learning analysis", BJS Open, 2024;8.
  • M. van Rijthoven, S. Obahor, F. Pagliarulo, V. den Maries, P. Schraml, H. Moch, J. van der Laak, F. Ciompi and K. Silina, "Multi-resolution deep learning characterizes tertiary lymphoid structures and their prognostic relevance in solid tumors", Communications Medicine, 2024.
  • N. Marini, S. Marchesin, M. Wodzinski, A. Caputo, D. Podareanu, B. Guevara, S. Boytcheva, S. Vatrano, F. Fraggetta, F. Ciompi, G. Silvello, H. Müller and M. Atzori, "Multimodal representations of biomedical knowledge from limited training whole slide images and reports using deep learning", Medical Image Analysis, 2024;97:103303.
  • Y. Jiao, J. van der Laak, S. Albarqouni, Z. Li, T. Tan, A. Bhalerao, J. Ma, J. Sun, J. Pocock, J. Pluim, N. Koohbanani, R. Bashir, S. Raza, S. Liu, S. Graham, S. Wetstein, S. Khurram, T. Watson, N. Rajpoot, M. Veta and F. Ciompi, "LYSTO: The Lymphocyte Assessment Hackathon and Benchmark Dataset", IEEE Journal of Biomedical and Health Informatics, 2023:1-12.
  • N. Marini, S. Marchesin, S. Otalora, M. Wodzinski, A. Caputo, M. van Rijthoven, W. Aswolinskiy, J. Bokhorst, D. Podareanu, E. Petters, S. Boytcheva, G. Buttafuoco, S. Vatrano, F. Fraggetta, J. van der Laak, M. Agosti, F. Ciompi, G. Silvello, H. Muller and M. Atzori, "Unleashing the potential of digital pathology data by training computer-aided diagnosis models without human annotations.", NPJ digital medicine, 2022;5(1):102.
  • M. van Rijthoven, M. Balkenhol, K. Silina, J. van der Laak and F. Ciompi, "HookNet: Multi-resolution convolutional neural networks for semantic segmentation in histopathology whole-slide images", Medical Image Analysis, 2021;68:101890.
  • D. Tellez, G. Litjens, J. van der Laak and F. Ciompi, "Neural Image Compression for Gigapixel Histopathology Image Analysis.", IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021;43(2):567-578.