Iterative Augmentation of Visual Evidence for Weakly-Supervised Lesion Localization in Deep Interpretability Frameworks: Application to Color Fundus Images

@article{GonzlezGonzalo2020IterativeAO,
  title={Iterative Augmentation of Visual Evidence for Weakly-Supervised Lesion Localization in Deep Interpretability Frameworks: Application to Color Fundus Images},
  author={Cristina Gonz{\'a}lez-Gonzalo and Bart Liefers and Bram van Ginneken and Clara I. S{\'a}nchez},
  journal={IEEE Transactions on Medical Imaging},
  year={2020},
  volume={39},
  pages={3499-3511}
}
Interpretability of deep learning (DL) systems is gaining attention in medical imaging to increase experts’ trust in the obtained predictions and facilitate their integration in clinical settings. We propose a deep visualization method to generate interpretability of DL classification tasks in medical imaging by means of visual evidence augmentation. The proposed method iteratively unveils abnormalities based on the prediction of a classifier trained only with image-level labels. For each image… 
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