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[PTC18a] Handling Missing Annotations for Semantic Segmentation with Deep ConvNets.
Atelier, Poster ou Démonstration dans une Conférence Internationale : 4th Workshop on Deep Learning in Medical Image Analysis, MICCAI, September 2018, Granada, Spain, (DOI: 10.1007/978-3-030-00889-5_3)Mots clés: Medical Images, Deep Learning, Convolutional Neural Networks, Incomplete Ground Truth Annotation, Noisy Labels, Missing Labels.
Résumé:
Annotation of medical images for semantic segmentation is
a very time consuming and dicult task. Moreover, clinical experts of-
ten focus on speci c anatomical structures and thus, produce partially
annotated images. In this paper, we introduce SMILE, a new deep con-
volutional neural network which addresses the issue of learning with in-
complete ground truth. SMILE aims to identify ambiguous labels in or-
der to ignore them during training, and don't propagate incorrect or
noisy information. A second contribution is SMILEr which uses SMILE
as initialization for automatically relabeling missing annotations, using
a curriculum strategy. Experiments on 3 organ classes (liver, stomach,
pancreas) show the relevance of the proposed approach for semantic seg-
mentation: with 70% of missing annotations, SMILEr performs similarly
as a baseline trained with complete ground truth annotations.
BibTeX
| @inproceedings { | |||
| PTC18a, | |||
| title | = | "{Handling Missing Annotations for Semantic Segmentation with Deep ConvNets.}", | |
| author | = | " O. Petit and N. Thome and A. Charnoz and A. Hostettler and L. Soler ", | |
| booktitle | = | "{4th Workshop on Deep Learning in Medical Image Analysis, MICCAI}", | |
| year | = | 2018, | |
| month | = | "September", | |
| address | = | "Granada, Spain", | |
| doi | = | "10.1007/978-3-030-00889-5_3", | |
| } | |||
