# Deep Learning with Mixed Supervision for Brain Tumor Segmentation

arXiv: https://arxiv.org/abs/1812.04571 · HTML: https://mlsanity.com/paper/1812.04571/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Dec 2018.

## Built on by (most cited first, 18 of 18)

- [Mixed supervision for surface-defect detection: from weakly to fully supervised learning](https://mlsanity.com/paper/2104.06064/) (2021)
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- [Mirror U-Net: Marrying Multimodal Fission with Multi-task Learning for Semantic Segmentation in Medical Imaging](https://mlsanity.com/paper/2303.07126/) (2023)
- [Segmentation with mixed supervision: Confidence maximization helps knowledge distillation](https://mlsanity.com/paper/2109.10902/) (2021)
- [Hybrid Supervision Learning for Pathology Whole Slide Image Classification](https://mlsanity.com/paper/2107.00934/) (2021)
- [A Systematic Approach for MRI Brain Tumor Localization, and Segmentation using Deep Learning and Active Contouring](https://mlsanity.com/paper/2102.03532/) (2021)
- [On the Efficiency of Subclass Knowledge Distillation in Classification Tasks](https://mlsanity.com/paper/2109.05587/) (2021)
- [Multi-task Learning To Improve Semantic Segmentation Of CBCT Scans Using Image Reconstruction](https://mlsanity.com/paper/2312.12990/) (2023)
- [Subclass Knowledge Distillation with Known Subclass Labels](https://mlsanity.com/paper/2207.08063/) (2022)
- [HALOS: Hallucination-free Organ Segmentation after Organ Resection Surgery](https://mlsanity.com/paper/2303.07717/) (2023)
- [Building Brain Tumor Segmentation Networks with User-Assisted Filter Estimation and Selection](https://mlsanity.com/paper/2403.12748/) (2024)
- [TextFormer: A Query-based End-to-End Text Spotter with Mixed Supervision](https://mlsanity.com/paper/2306.03377/) (2023)
- [Segmentation-Consistent Probabilistic Lesion Counting](https://mlsanity.com/paper/2204.05276/) (2022)
- [Teacher-Student Architecture for Mixed Supervised Lung Tumor Segmentation](https://mlsanity.com/paper/2112.11541/) (2021)
- [SP 3 : Superpixel-propagated pseudo-label learning for weakly semi-supervised medical image segmentation](https://mlsanity.com/paper/2411.11636/) (2024)
- [Deep Learning Approaches for Medical Imaging Under Varying Degrees of Label Availability: A Comprehensive Survey](https://mlsanity.com/paper/2504.11588/) (2025)
- [WILLIE: A Unified Framework and Benchmark for Wound Classification, Segmentation, and Localization](https://mlsanity.com/paper/2610.05341/) (2026)

## Abstract (arXiv)

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 segmentation in Magnetic Resonance images from BRATS 2018 challenge. We show that the proposed approach provides a significant improvement of segmentation 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.