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.
@article{arxiv.1911.01738,
title = {Weakly Supervised Fine Tuning Approach for Brain Tumor Segmentation Problem},
author = {Sergey Pavlov and Alexey Artemov and Maksim Sharaev and Alexander Bernstein and Evgeny Burnaev},
journal= {arXiv preprint arXiv:1911.01738},
year = {2019}
}
Comments
Accepted to IEEE International Conference on Machine Learning and Applications (ICMLA 2019). Typos corrected, images updated