# Joint Prediction of Meningioma Grade and Brain Invasion via Task-Aware Contrastive Learning

arXiv: https://arxiv.org/abs/2209.01517 · HTML: https://mlsanity.com/paper/2209.01517/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 32 references that are in the map.
- First posted on arXiv 4 Sep 2022.

## Builds on (most cited first, 8 of 8)

- [Multi-Task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semantics](https://mlsanity.com/paper/1705.07115/) (2017)
- [GradNorm: Gradient Normalization for Adaptive Loss Balancing in Deep Multitask Networks](https://mlsanity.com/paper/1711.02257/) (2017)
- [Data-Efficient Image Recognition with Contrastive Predictive Coding](https://mlsanity.com/paper/1905.09272/) (2019)
- [Propagate Yourself: Exploring Pixel-Level Consistency for Unsupervised Visual Representation Learning](https://mlsanity.com/paper/2011.10043/) (2020)
- [Exploring Cross-Image Pixel Contrast for Semantic Segmentation](https://mlsanity.com/paper/2101.11939/) (2021)
- [Disentangled Representation Learning in Cardiac Image Analysis](https://mlsanity.com/paper/1903.09467/) (2019)
- [Modality-aware Mutual Learning for Multi-modal Medical Image Segmentation](https://mlsanity.com/paper/2107.09842/) (2021)
- [Delving into Inter-Image Invariance for Unsupervised Visual Representations](https://mlsanity.com/paper/2008.11702/) (2020)

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

- [Towards multi-modal anatomical landmark detection for ultrasound-guided brain tumor resection with contrastive learning](https://mlsanity.com/paper/2307.14523/) (2023)
- [OWT: A Foundational Organ-Wise Tokenization Framework for Medical Imaging](https://mlsanity.com/paper/2505.04899/) (2025)

## Abstract (arXiv)

Preoperative and noninvasive prediction of the meningioma grade is important in clinical practice, as it directly influences the clinical decision making. What's more, brain invasion in meningioma (i.e., the presence of tumor tissue within the adjacent brain tissue) is an independent criterion for the grading of meningioma and influences the treatment strategy. Although efforts have been reported to address these two tasks, most of them rely on hand-crafted features and there is no attempt to exploit the two prediction tasks simultaneously. In this paper, we propose a novel task-aware contrastive learning algorithm to jointly predict meningioma grade and brain invasion from multi-modal MRIs. Based on the basic multi-task learning framework, our key idea is to adopt contrastive learning strategy to disentangle the image features into task-specific features and task-common features, and explicitly leverage their inherent connections to improve feature representation for the two prediction tasks. In this retrospective study, an MRI dataset was collected, for which 800 patients (containing 148 high-grade, 62 invasion) were diagnosed with meningioma by pathological analysis. Experimental results show that the proposed algorithm outperforms alternative multi-task learning methods, achieving AUCs of 0:8870 and 0:9787 for the prediction of meningioma grade and brain invasion, respectively. The code is available at https://github.com/IsDling/predictTCL.