A Decade of GigaScience: The Challenges of Gigapixel Pathology Images

Abstract

In the last decade, the field of computational pathology has advanced at a rapid pace because of the availability of deep neural networks, which achieved their first successes in computer vision tasks in 2012. An important driver for the progress of the field were public competitions, so called ‘Grand Challenges’, in which increasingly large data sets were offered to the public to solve clinically relevant tasks. Going from the first Pathology challenges, which had data obtained from 23 patients, to current challenges sharing data of thousands of patients, performance of developed deep learning solutions has reached (and sometimes surpassed) the level of experienced pathologists for specific tasks. We expect future challenges to broaden the horizon, for instance by combining data from radiology, pathology and tumor genetics, and to extract prognostic and predictive information independent of currently used grading schemes.

Next-Generation Morphometry for pathomics-data mining in histopathology

“…Additionally, the format of the clinical data differed, not allowing to combine all cohorts in all analyses. Current initiatives, such as the BIGPICTURE project 36 , or grand challenges 37 might help to tackle this in the future. We openly provide our pathomics datasets for KPMP and HuBMAP, enriching these publicly available cohorts with complementary pathomics data.…”

Accelerating progress in the social sciences: the potential of benchmarks

“…In computational pathology, opening up large datasets with clinically relevant tasks to the general public through challenges was identified as a key driver of progress in the field (Hartman et al, 2020). This progress led to machine learning approaches outperforming humans on some of these tasks Litjens, Ciompi, and van der Laak (2022).…”