Weakly Supervised Fine Tuning Approach for Brain Tumor Segmentation Problem

TL;DR

This paper introduces a weakly supervised learning framework that leverages both pixel-level and image-level annotations to improve brain tumor segmentation in MRI images, reducing the need for costly pixel-level labels.

Contribution

A novel framework that combines pixel- and image-level annotations for brain tumor segmentation, demonstrating competitive performance with traditional fully-supervised methods.

Findings

The proposed method effectively utilizes both annotation types.

Performance is competitive with fully-supervised approaches.

The influence of annotation types on segmentation quality is analyzed.

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.