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One of the most important challenges for computer-aided analysis in digital pathology is the development of robust deep neural networks, which can cope with variations in color and resolution of digitized whole-slide images (WSIs). It has been shown that color augmentation during training is a useful method to aid a model generalize better to heterogeneous data. In this work, we compare state of the art models EfficientNet, Xception, Inception, ResNet, DenseNet, MobileNet and QuickNet on a multi-scanner database comprising slides each digitized with six different scanners. All of the networks are trained with data of only one scanner applying a combination of color and blur augmentation techniques. All models show similar tendencies across the different scanner databases but differ in the overall classification accuracy. Differences in training and inference time, however, are more pronounced: on a mid-range GPU, the inference time of the fastest model (QuickNet) is 13 times faster than the slowest one (EfficientNet B4). There is also a trade-off between speed and accuracy, the slower networks are more stable across different scanners and show the overall best performance. A good compromise between quality and inference time is achieved by EfficientNet B0.
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Wednesday 7th July
B1-9 (short): Application: Histopathology - 13:45 - 14:30 (UTC+2)
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