Our work on “Data-driven color augmentation for H&E stained images in computational pathology“, led by Niccolò Marini, et al. has been published in the Elsevier Journal of Pathology Informatics.
In this work, we present a method to improve the efficiency of color augmentation methods by increasing the reliability of the augmented samples used for training computational pathology models. During CNN training, a database including over 2 million H&E color variations collected from private and public datasets is used as a reference to discard augmented data with color distributions that do not correspond to realistic data.
The GitHub repository with the code is available online : https://lnkd.in/eY8byYcR ,
and the database including stain vectors: https://lnkd.in/eNf3bnTu
