par Foucart, Adrien 
;Debeir, Olivier ;Decaestecker, Christine
Référence (April 8-11, 2019: Venice, Italy), 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019), IEEE, page (1869-1872)
Publication Publié, 2019-04-11
;Debeir, Olivier ;Decaestecker, Christine Référence (April 8-11, 2019: Venice, Italy), 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019), IEEE, page (1869-1872)
Publication Publié, 2019-04-11
Publication dans des actes
| Résumé : | Digital pathology produces a lot of images. For machine learning applications, these images need to be annotated, which can be complex and time consuming. Therefore, outside of a few benchmark datasets, real-world applications often rely on data with scarce or unreliable annotations. Inthis paper, we quantitatively analyze how different types of perturbations influence the results of a typical deep learning algorithm by artificially weakening the annotations of a benchmark biomedical dataset. We use classical machine learning paradigms (semi-supervised, noisy and weak learning) adapted to deep learning to try to counteract those effects, and analyze the effectiveness of these methods in addressing different types of weakness. |
