Wisdom of the crowd for early detection in barrett's esophagus

Justin Law, Thomas G. Paulson, Carissa A. Sanchez, Patricia C. Galipeau, Marnix Jansen, Matthew D. Stachler, Carlo C. Maley, Yinyin Yuan

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Cell detection is an essential task for characterizing and studying tumor microenvironments (TME). Automatic cell detection in histopathology is challenging due to the diversity of cell shape, size, morphology, as well as stain variations between laboratories. Though deep learning has become the choice method to tackle this task, typically training requires a large number of annotations, which can be laborious and time consuming. While recent developments to tackle this annotation problem have seen success, typically these pipelines add complexity to training and may not be easy to implement. In this paper we demonstrate that using several public datasets we can train a competitive cell detection network for Barrett's Esophagus (BE) premalignant tissue samples using conventional supervised training methods. To adapt the network to clinical BE tissue sections, pseudolabels were generated to retrain the network. The results indicate that public cell detection datasets can be used to train networks that generalize well to pre-cancer tissue samples without requiring any manual annotations, which can accelerate digital pathology research for early detection.

Original languageEnglish (US)
Title of host publication2021 IEEE 18th International Symposium on Biomedical Imaging, ISBI 2021
PublisherIEEE Computer Society
Number of pages5
ISBN (Electronic)9781665412469
StatePublished - Apr 13 2021
Event18th IEEE International Symposium on Biomedical Imaging, ISBI 2021 - Nice, France
Duration: Apr 13 2021Apr 16 2021

Publication series

NameProceedings - International Symposium on Biomedical Imaging
ISSN (Print)1945-7928
ISSN (Electronic)1945-8452


Conference18th IEEE International Symposium on Biomedical Imaging, ISBI 2021


  • Barrett's esophagus
  • Cell detection
  • Convolutional neural network
  • Deep learning
  • Histopathology
  • Pre-cancer

ASJC Scopus subject areas

  • Biomedical Engineering
  • Radiology Nuclear Medicine and imaging


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