NucDETR: End-to-End Transformer for Nucleus Detection in Histopathology Images

Ahmad Obeid, Taslim Mahbub, Sajid Javed, Jorge Dias, Naoufel Werghi

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

6 Scopus citations

Abstract

Nucleus detection in histopathology images is an instrumental step for the assessment of a tumor. Nonetheless, nucleus detection is a laborious and expensive task if done manually by experienced clinicians, and is also prone to subjectivity and inconsistency. Alternatively, the advancement in computer vision-based analysis enables the automatic detection of cancerous nuclei; however, the task poses several challenges due to the heterogeneity in the morphology and color of the nuclei, their varying chromatin distribution, and their fuzzy boundaries. In this work, we propose the usage of transformer-based detection, and dub it NucDETR, to tackle this problem, given their promising results and simple architecture on several tasks including object detection. We inspire from the recently-proposed Detection Transformer (DETR), and propose the introduction of a necessary data synthesis step; demonstrating its effectiveness and benchmarking the performance of Transformer detectors on histopathology images. Where applicable, we also propose remedies that mitigate some of the issues faced when adopting such Transformer-based detection. The proposed end-to-end architecture avoids much of the post-processing steps demanded by most current detectors, and outperforms the state-of-the-art methods on two popular datasets by 1–9% in the F-score.

Original languageBritish English
Title of host publicationComputational Mathematics Modeling in Cancer Analysis - 1st International Workshop, CMMCA 2022, Held in Conjunction with MICCAI 2022, Proceedings
EditorsWenjian Qin, Nazar Zaki, Fa Zhang, Jia Wu, Fan Yang
PublisherSpringer Science and Business Media Deutschland GmbH
Pages47-57
Number of pages11
ISBN (Print)9783031172656
DOIs
StatePublished - 2022
Event1st International Workshop on Computational Mathematics Modeling in Cancer Analysis, CMMCA 2022, held in conjunction with the 25th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2022 - Virtual, Online
Duration: 18 Sep 202218 Sep 2022

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13574 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference1st International Workshop on Computational Mathematics Modeling in Cancer Analysis, CMMCA 2022, held in conjunction with the 25th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2022
CityVirtual, Online
Period18/09/2218/09/22

Keywords

  • Computational histopathology
  • Nucleus detection
  • Transformer-based detection

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