Deep learning analysis of contrast-enhanced spectral mammography to determine histoprognostic factors of malignant breast tumours.
Caroline DominiqueFrançoise CallonnecAnca BerghianDiana DeftaPierre VeraRomain ModzelewskiPierre DecazesPublished in: European radiology (2022)
• A deep learning model developed for chest radiography was adapted by fine-tuning to be used on contrast-enhanced spectral mammography. • The adapted models allowed to determine for invasive breast cancers the status of estrogen receptors and triple-negative receptors. • Such models applied to contrast-enhanced spectral mammography could provide rapid prognostic and predictive information.
Keyphrases
- contrast enhanced
- deep learning
- dual energy
- optical coherence tomography
- diffusion weighted
- magnetic resonance imaging
- computed tomography
- magnetic resonance
- image quality
- diffusion weighted imaging
- artificial intelligence
- convolutional neural network
- machine learning
- estrogen receptor
- cone beam computed tomography
- quantum dots
- sensitive detection
- loop mediated isothermal amplification