Christine Decaestecker (ULB) - "Deep learning from multi-expert annotations: need for prior consensus or not? A use case in prostate cancer classification"
Deep learning algorithms rely on large amounts of annotations for the training and validation stages. In the medical image domain, the "ground truth" is rarely available and disagreements between experts affect many segmentation and classification tasks. Often, consensus annotations are produced as "ground truth" for training and performance evaluation. This talk presents a use case in digital pathology (prostate cancer grading) where taking into account the annotations of each expert can be beneficial for learning and interpreting results, while being more consistent with the complex clinical reality.