Open research

As a researcher, I strive to make access to science and research as easy as possible for everyone. Open science in applied machine learning research involves open-sourcing code to ensure reproducibility and transparency of new methods, as well as publishing datasets for robust comparative analysis.

• HistoCartography: A collection of image-to-graph translation and state-of-the-art graph algorithms for facilitating interpretable entity-based analysis in digital pathology [Code]

• BReAst Carcinoma Subtyping (BRACS): A large cohort of H&E stained histopathological images for automated breast cancer diagnosis [Website]

• FUNSD: A dataset for Form Understanding in Noisy Scanned Documents [Website]