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Semantic Segmentation of Histopathological Slides for the Classification of Cutaneous Lymphoma and Eczema

Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1248)

Abstract

Mycosis fungoides (MF) is a rare, potentially life threatening skin disease, which in early stages clinically and histologically strongly resembles Eczema, a very common and benign skin condition. In order to increase the survival rate, one needs to provide the appropriate treatment early on. To this end, one crucial step for specialists is the evaluation of histopathological slides (glass slides), or Whole Slide Images (WSI), of the patients’ skin tissue. We introduce a deep learning aided diagnostics tool that brings a two-fold value to the decision process of pathologists. First, our algorithm accurately segments WSI into regions that are relevant for an accurate diagnosis, achieving a Mean-IoU of \(69\%\) and a Matthews Correlation score of \(83\%\) on a novel dataset. Additionally, we also show that our model is competitive with the state of the art on a reference dataset. Second, using the segmentation map and the original image, we are able to predict if a patient has MF or Eczema. We created two models that can be applied in different stages of the diagnostic pipeline, potentially eliminating life-threatening mistakes. The classification outcome is considerably more interpretable than using only the WSI as the input, since it is also based on the segmentation map. Our segmentation model, which we call EU-Net, extends a classical U-Net with an EfficientNet-B7 encoder which was pre-trained on the Imagenet dataset.

Keywords

Semantic segmentation Histopathological slides Cutaneous lymphoma Eczema U-Net EfficientNet Transfer learning Classification 

Notes

Acknowledgements

We would like to thank everybody at the Kempf und Pfaltz Histologische Diagnostik lab that provided crucial insights about the task, all the necessary data and for their time and commitment to this collaboration. Finally, we would like to thank PAIGE (paige.ai) for providing the software that was used to annotate the WSI.

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.ETH ZürichZürichSwitzerland
  2. 2.Kempf und Pfaltz Histologische DiagnostikZürichSwitzerland

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