Co-occurring diseases heavily influence the performance of weakly supervised learning models for classification of chest CT

Fakrul Islam Tushar, Vincent M. Danniballe, Geoffrey D. Rubin, Ehsan Samei, Joseph Y. Lo

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


Despite the potential of weakly supervised learning to automatically annotate massive amounts of data, little is known about its limitations for use in computer-aided diagnosis (CAD). For CT specifically, interpreting the performance of CAD algorithms can be challenging given the large number of co-occurring diseases. This paper examines the effect of co-occurring diseases when training classification models by weakly supervised learning, specifically by comparing multi-label and multiple binary classifiers using the same training data. Our results demonstrated that the binary model outperformed the multi-label classification in every disease category in terms of AUC. However, this performance was heavily influenced by co-occurring diseases in the binary model, suggesting it did not always learn the correct appearance of the specific disease. For example, binary classification of lung nodules resulted in an AUC of < 0.65 when there were no other co-occurring diseases, but when lung nodules cooccurred with emphysema, the performance reached AUC < 0.80. We hope this paper revealed the complexity of interpreting disease classification performance in weakly supervised models and will encourage researchers to examine the effect of co-occurring diseases on classification performance in the future.

Original languageEnglish (US)
Title of host publicationMedical Imaging 2022
Subtitle of host publicationComputer-Aided Diagnosis
EditorsKaren Drukker, Khan M. Iftekharuddin
ISBN (Electronic)9781510649415
StatePublished - 2022
EventMedical Imaging 2022: Computer-Aided Diagnosis - Virtual, Online
Duration: Mar 21 2022Mar 27 2022

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
ISSN (Print)1605-7422


ConferenceMedical Imaging 2022: Computer-Aided Diagnosis
CityVirtual, Online


  • CT
  • binary classifier
  • classification
  • co-occurring diseases
  • multi-label classifier
  • weak-supervision

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Atomic and Molecular Physics, and Optics
  • Biomaterials
  • Radiology Nuclear Medicine and imaging


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