Weakly supervised fine tuning approach for brain tumor segmentation problem

Sergey Pavlov1Alexey Artemov1Maxim Sharaev1Alexander Bernstein1Evgeny Burnaev1

1Skolkovo Institute of Science and Technology2Moscow Institute of Physics and Technology

International Conference On Machine Learning And Applications 2019 (ICMLA 2019)


Abstract—Segmentation of tumors in brain MRI images is a challenging task, where most recent methods demand large volumes of data with pixel-level annotations, which are generally costly to obtain. In contrast, image-level annotations, where only the presence of lesion is marked, are generally cheap, generated in far larger volumes compared to pixel-level labels, and contain less labeling noise. In the context of brain tumor segmentation, both pixel-level and image-level annotations are commonly available; thus, a natural question arises whether a segmentation procedure could take advantage of both. In the present work we: 1) propose a learning-based framework that allows simultaneous usage of both pixel- and image-level annotations in MRI images to learn a segmentation model for brain tumor; 2) study the influence of comparative amounts of pixel- and image-level annotations on the quality of brain tumor segmentation; 3) compare our approach to the traditional fully-supervised approach and show that the performance of our method in terms of segmentation quality may be competitive.