Deep Learning with Mixed Supervision for Brain Tumor Segmentation

Abstract : Most of the current state-of-the-art methods for tumor segmentation are based on machine learning models trained on manually segmented images. This type of training data is particularly costly, as manual delineation of tumors is not only time-consuming but also requires medical expertise. On the other hand, images with a provided global label (indicating presence or absence of a tumor) are less informative but can be obtained at a substantially lower cost. In this paper, we propose to use both types of training data (fully-annotated and weakly-annotated) to train a deep learning model for segmentation. The idea of our approach is to extend segmentation networks with an additional branch performing image-level classification. The model is jointly trained for segmentation and classification tasks in order to exploit information contained in weakly-annotated images while preventing the network to learn features which are irrelevant for the segmentation task. We evaluate our method on the challenging task of brain tumor seg-mentation in Magnetic Resonance images from BRATS 2018 challenge. We show that the proposed approach provides a significant improvement of seg-mentation performance compared to the standard supervised learning. The observed improvement is proportional to the ratio between weakly-annotated and fully-annotated images available for training.
Document type :
Preprints, Working Papers, ...
Complete list of metadatas

https://hal.inria.fr/hal-01952458
Contributor : Pawel Mlynarski <>
Submitted on : Thursday, December 13, 2018 - 3:29:31 PM
Last modification on : Friday, April 19, 2019 - 4:54:50 PM
Long-term archiving on : Thursday, March 14, 2019 - 12:40:08 PM

File

Pawel_Mlynarski_Mixed_Supervis...
Files produced by the author(s)

Identifiers

  • HAL Id : hal-01952458, version 1
  • ARXIV : 1812.04571

Collections

Citation

Pawel Mlynarski, Hervé Delingette, Antonio Criminisi, Nicholas Ayache. Deep Learning with Mixed Supervision for Brain Tumor Segmentation. 2018. ⟨hal-01952458⟩

Share

Metrics

Record views

93

Files downloads

107