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Robust Slide Cartography in Colon Cancer Histology: Evaluation on a Multi-scanner Database

 
: Kuritcyn, P.; Geppert, C.I.; Eckstein, M.; Hartmann, A.; Wittenberg, T.; Dexl, J.; Baghdadlian, S.; Hartmann, D.; Perrin, D.; Bruns, V.; Benz, M.

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Palm, Christoph (Hrsg.):
Bildverarbeitung für die Medizin 2021 : Proceedings, German Workshop on Medical Image Computing, Regensburg, March 7-9, 2021
Wiesbaden: Springer Fachmedien, 2021 (Informatik aktuell)
ISBN: 978-3-658-33197-9 (Print)
ISBN: 978-3-658-33198-6 (Online)
ISBN: 3-658-33197-6
pp.229-234
German Workshop on Medical Image Computing <2021, Regensburg>
Workshop Bildverarbeitung für die Medizin (BVM) <2021, Regensburg>
English
Conference Paper
Fraunhofer IIS ()

Abstract
Robustness against variations in color and resolution of digitized whole-slide images (WSIs) is an essential requirement for any computer-aided analysis in digital pathology. One common approach to encounter a lack of heterogeneity in the training data is data augmentation. We investigate the impact of different augmentation techniques for whole-slide cartography in colon cancer histology using a newly created multi-scanner database of 39 slides each digitized with six different scanners. A state of the art convolutional neural network (CNN) is trained to differentiate seven tissue classes. Applying a model trained on one scanner to WSIs acquired with a different scanner results in a significant decrease in classification accuracy. Our results show that the impact of resolution variations is less than of color variations: the accuracy of the baseline model trained without any augmentation at all is 73% for WSIs with similar color but different resolution against 35% for WSIs with similar resolution but color deviations. The grayscale model shows comparatively robust results and evades the problem of color variation. A combination of multiple color augmentations methods lead to a significant overall improvement (between 33 and 54 percentage points). Moreover, fine-tuning a pre-trained network using a small amount of annotated data from new scanners benefits the performance for these particular scanners, but this effect does not generalize to other unseen scanners.

: http://publica.fraunhofer.de/documents/N-637669.html