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

TL;DR

This paper presents EU-Net, a deep learning model that segments histopathological slides to distinguish between Mycosis fungoides and Eczema, improving diagnostic accuracy and interpretability in skin disease classification.

Contribution

Introduction of EU-Net, a novel segmentation model combining U-Net with EfficientNet-B7, for accurate and interpretable classification of skin diseases from histopathological images.

Findings

Achieved 69% Mean-IoU and 83% Matthews Correlation on novel dataset.

Model is competitive with state-of-the-art on reference dataset.

Segmentation improves diagnostic interpretability.

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.