# Multimodal brain tumor classification

arXiv: https://arxiv.org/abs/2009.01592 · HTML: https://mlsanity.com/paper/2009.01592/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Sep 2020.

## Built on by (most cited first, 4 of 4)

- [Giga-SSL: Self-Supervised Learning for Gigapixel Images](https://mlsanity.com/paper/2212.03273/) (2022)
- [Efficient subtyping of ovarian cancer histopathology whole slide images using active sampling in multiple instance learning](https://mlsanity.com/paper/2302.08867/) (2023)
- [Multiple Instance Learning with random sampling for Whole Slide Image Classification](https://mlsanity.com/paper/2403.05351/) (2024)
- [DGG-XNet: A Hybrid Deep Learning Framework for Multi-Class Brain Disease Classification with Explainable AI](https://mlsanity.com/paper/2506.14367/) (2025)

## Abstract (arXiv)

Cancer is a complex disease that provides various types of information depending on the scale of observation. While most tumor diagnostics are performed by observing histopathological slides, radiology images should yield additional knowledge towards the efficacy of cancer diagnostics. This work investigates a deep learning method combining whole slide images and magnetic resonance images to classify tumors. In particular, our solution comprises a powerful, generic and modular architecture for whole slide image classification. Experiments are prospectively conducted on the 2020 Computational Precision Medicine challenge, in a 3-classes unbalanced classification task. We report cross-validation (resp. validation) balanced-accuracy, kappa and f1 of 0.913, 0.897 and 0.951 (resp. 0.91, 0.90 and 0.94). For research purposes, including reproducibility and direct performance comparisons, our finale submitted models are usable off-the-shelf in a Docker image available at https://hub.docker.com/repository/docker/marvinler/cpm_2020_marvinler.