$\mu$PEN: Multi-class PseudoEdgeNet for PD-L1 assessment

Jeroen Vermazeren, Leander van Eekelen, Luca Dulce Meesters, Monika Looijen-Salamon, Shoko Vos, Enrico Munari, Caner Mercan, Francesco Ciompi

Keywords: PseudoEdgeNet, YOLO, PD-L1, lung cancer, histopathology

TL;DR: We extend PseudoEdgeNet for the multi-class task of PD-L1 stained cell detection in non-small cell lung cancer histopathology images and benchmark it against YoloV5.

Abstract: In this paper, we take the recently presented PseudoEdgeNet model to the level of multi-class cell segmentation in histopathology images solely trained with point annotations. We tailor its loss function to the challenges of multi-class segmentation and equip it with an additional false positive loss term. We evaluate it on the assessment of tumor and immune cells in PD-L1 stained lung cancer histopathology images, and compare it with YOLOv5.

Paper Type: validation/application paper

Primary Subject Area: Detection and Diagnosis

Secondary Subject Area: Application: Histopathology

Paper Status: original work, not submitted yet

Source Code Url:

All code used for the YoloV5 model was directly taken from the open-source Github repo for Yolov5 by Ultralytics: https://github.com/ultralytics/yolov5

The code used for the PseudoEdgeNet model is currently a work in progress and contains multiple novel contributions that we do not fully elaborate on in this paper.

Data Set Url: The work in this paper is fully based on a dataset collected in-house at our hospital - we currently do not have permission to share this data in an open-source way.

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