Case study
May 21, 2023

Case study: Histomics

Authors
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Case study
May 21, 2023

Case study: Histomics

Authors
No items found.
About Owkin

Owkin is an AI biotechnology company that uses AI to find the right treatment for every patient. We combine the best of human and artificial intelligence to answer the research questions shared by biopharma and academic researchers. By closing the translational gap between complex biology and new treatments, we bring new diagnostics and drugs to patients sooner.

We use AI to identify new treatments, de-risk and accelerate clinical trials and build diagnostic tools. Using federated learning, a pioneering collaborative AI framework, Owkin enables partners to unlock valuable insights from siloed datasets while protecting patient privacy and securing proprietary data.

Owkin was co-founded by Thomas Clozel MD, a former assistant professor in clinical onco-hematology, and Gilles Wainrib, a pioneer in the field of machine learning in biology, in 2016. Owkin has raised over $300 million and became a unicorn through investments from leading biopharma companies (Sanofi and BMS) and venture funds (Fidelity, GV and BPI, among others).

Case study: Histomics

Revealing interpretable signatures of whole slide images

Inclusion criteria models
Screening biomarker
Histology Data
Pan Cancer
Context

Detecting different tissue (e.g. tumor, muscle, fibrosis) and cell types (e.g. B cells, T cells, plasma cells) from digitized whole slide images (WSI) would greatly facilitate patient diagnosis, patient inclusion to new clinical trials, and support prediction of response to treatment.

Methods

We use pathologist’s annotation of tissues to train AI models called Histomics.

  • Pixel-level labels
  • Tile-level labels

Results

Histomics serve as interpretable features to:

  • Feed machine learning models
  • Decypher AI-based biomarkers
  • Characterize patients
  • Identify patterns in subgroups of patients

Impact

To identify novel biomarkers in images of specific tumoral regions that are important to better understand disease evolution and differentiated outcomes.

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