_:n6004393b30b04ac5946ecd0c32b7cc5bxb40
<http://schema.org/description>
"Stain variation is a phenomenon observed when distinct pathology laboratories\nstain tissue slides that exhibit similar but not identical color appearance.\nDue to this color shift between laboratories, convolutional neural networks\n(CNNs) trained with images from one lab often underperform on unseen images\nfrom the other lab. Several techniques have been proposed to reduce the\ngeneralization error, mainly grouped into two categories: stain color\naugmentation and stain color normalization. The former simulates a wide variety\nof realistic stain variations during training, producing stain-invariant CNNs.\nThe latter aims to match training and test color distributions in order to\nreduce stain variation. For the first time, we compared some of these\ntechniques and quantified their effect on CNN classification performance using\na heterogeneous dataset of hematoxylin and eosin histopathology images from 4\norgans and 9 pathology laboratories. Additionally, we propose a novel\nunsupervised method to perform stain color normalization using a neural\nnetwork. Based on our experimental results, we provide practical guidelines on\nhow to use stain color augmentation and stain color normalization in future\ncomputational pathology applications."
<https://deepai.org/profile/wouter-bulten>