Kavli Affiliate: Changhuei Yang
| First 5 Authors: Siyu, Lin, Haowen Zhou, Richard J. Cote, Mark Watson
| Summary:
In recent years, deep neural networks (DNNs) have demonstrated remarkable
performance in pathology applications, potentially even outperforming expert
pathologists due to their ability to learn subtle features from large datasets.
One complication in preparing digital pathology datasets for DNN tasks is
variation in tinctorial qualities. A common way to address this is to perform
stain normalization on the images. In this study, we show that a well-trained
DNN model trained on one batch of histological slides failed to generalize to
another batch prepared at a different time from the same tissue blocks, even
when stain normalization methods were applied. This study used sample data from
a previously reported DNN that was able to identify patients with early stage
non-small cell lung cancer (NSCLC) whose tumors did and did not metastasize,
with high accuracy, based on training and then testing of digital images from
H&E stained primary tumor tissue sections processed at the same time. In this
study we obtained a new series of histologic slides from the adjacent recuts of
same tissue blocks processed in the same lab but at a different time. We found
that the DNN trained on the either batch of slides/images was unable to
generalize and failed to predict progression in the other batch of
slides/images (AUC_cross-batch = 0.52 – 0.53 compared to AUC_same-batch = 0.74
– 0.81). The failure to generalize did not improve even when the tinctorial
difference correction were made through either traditional color-tuning or
stain normalization with the help of a Cycle Generative Adversarial Network
(CycleGAN) process. This highlights the need to develop an entirely new way to
process and collect consistent microscopy images from histologic slides that
can be used to both train and allow for the general application of predictive
DNN algorithms.
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