# Weakly Supervised Fine Tuning Approach for Brain Tumor Segmentation Problem

arXiv: https://arxiv.org/abs/1911.01738 · HTML: https://mlsanity.com/paper/1911.01738/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Nov 2019.

## Built on by (most cited first, 2 of 2)

- [Biologically Inspired Deep Learning Approaches for Fetal Ultrasound Image Classification](https://mlsanity.com/paper/2506.08623/) (2025)
- [HAPS: Rethinking Image Similarity for Virtual Staining](https://mlsanity.com/paper/2605.20362/) (2026)

## Abstract (arXiv)

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.