BIGPICTURE

Background

Developments in high-throughput slide scanning and data storage have revolutionised the Pathology field by enabling whole slide imaging (WSI) of histopathological specimens. Combined with the unprecedented possibilities of recent artificial intelligence (AI) techniques such as deep learning (DL) and hardware, we are now on the verge of accelerating “AI Pathology” and spur the use thereof across the entire value chain – from drug discovery towards clinical diagnostics. However, there are clearly several challenges that need to be overcome regarding large-scale development, validation and adoption of AI in Pathology.

Aim

BIGPICTURE’s vision is to become the catalyst in the digital transformation of pathology by creating and continuously enhancing the leading European repository, where both high-quality, annotated pathology data and AI algorithms will co-exist. The use of artificial intelligence in pathology will play a major role in:

  • Refining our knowledge of diseases
  • Finding better treatments
  • Improving diagnostic accuracy and efficiency
  • Helping to replace, reduce and refine animal research

BIGPICTURE’s repository will make pathology data and AI tools widely available through an international, inclusive community of experts in all relevant fields. This will pave the way for computational pathology.

Funding

BIGPICTURE is a public-private partnership funded by the EU Innovative Medicines Initiative (IMI) bringing together academic institutions, small- and medium-sized enterprises, public organizations, pharmaceutical companies, and a large network of partners.

People

Katrien Grünberg

Katrien Grünberg

Professor

Pathology, Radboud University Medical Center

Anna-Lena Frisk

Anna-Lena Frisk

Scientific Director Pathology

Janssen Pharmaceutica

Julie Boisclair

Julie Boisclair

Director Discovery and Investigative Pathology

Novartis

Francesco Ciompi

Francesco Ciompi

Associate Professor

Geert Litjens

Geert Litjens

Professor

Stephan Dooper

Stephan Dooper

PhD Candidate

Marina D'Amato

Marina D'Amato

PhD Candidate

Michelle Stegeman

Michelle Stegeman

PhD Candidate

Publications

  • J. van der Laak, G. Litjens and F. Ciompi, "Deep learning in histopathology: the path to the clinic.", Nature Medicine, 2021;27(5):775-784.
  • H. Pinckaers, B. van Ginneken and G. Litjens, "Streaming convolutional neural networks for end-to-end learning with multi-megapixel images", IEEE Transactions on Pattern Analysis and Machine Intelligence, 2020.