Generative Image Translation for Data Augmentation of Bone Lesion Pathology

Anant Gupta, Srivas Venkatesh, Sumit Chopra, Christian Ledig

Research output: Contribution to journalConference articlepeer-review


Insufficient training data and severe class imbalance are often limiting factors when developing machine learning models for the classification of rare diseases. In this work, we address the problem of classifying bone lesions from X-ray images by increasing the small number of positive samples in the training set. We propose a generative data augmentation approach based on a cycle-consistent generative adversarial network that synthesizes bone lesions on images without pathology. We pose the generative task as an image-patch translation problem that we optimize specifically for distinct bones (humerus, tibia, femur). In experimental results, we confirm that the described method mitigates the class imbalance problem in the binary classification task of bone lesion detection. We show that the augmented training sets enable the training of superior classifiers achieving better performance on a held-out test set. Additionally, we demonstrate the feasibility of transfer learning and apply a generative model that was trained on one body part to another.

Original languageEnglish (US)
Pages (from-to)225-235
Number of pages11
JournalProceedings of Machine Learning Research
StatePublished - 2019
Event2nd International Conference on Medical Imaging with Deep Learning, MIDL 2019 - London, United Kingdom
Duration: Jul 8 2019Jul 10 2019


  • Bone lesion
  • data augmentation
  • generative models
  • X-ray

ASJC Scopus subject areas

  • Artificial Intelligence
  • Software
  • Control and Systems Engineering
  • Statistics and Probability


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