# Deep Probabilistic Modeling of Glioma Growth

arXiv: https://arxiv.org/abs/1907.04064 · HTML: https://mlsanity.com/paper/1907.04064/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Jul 2019.

## Built on by (most cited first, 11 of 11)

- [A Review of Uncertainty Estimation and its Application in Medical Imaging](https://mlsanity.com/paper/2302.08119/) (2023)
- [Learning Spatio-Temporal Model of Disease Progression with NeuralODEs from Longitudinal Volumetric Data](https://mlsanity.com/paper/2211.04234/) (2022)
- [Treatment-aware Diffusion Probabilistic Model for Longitudinal MRI Generation and Diffuse Glioma Growth Prediction](https://mlsanity.com/paper/2309.05406/) (2023)
- [Continuous-Time Deep Glioma Growth Models](https://mlsanity.com/paper/2106.12917/) (2021)
- [Brain-WM: Brain Glioblastoma World Model](https://mlsanity.com/paper/2603.07562/) (2026)
- [An Uncertainty-aware Hierarchical Probabilistic Network for Early Prediction, Quantification and Segmentation of Pulmonary Tumour Growth](https://mlsanity.com/paper/2104.08789/) (2021)
- [Image prediction of disease progression by style-based manifold extrapolation](https://mlsanity.com/paper/2111.11439/) (2021)
- [Prediction of brain tumor recurrence location based on multi-modal fusion and nonlinear correlation learning](https://mlsanity.com/paper/2304.13725/) (2023)
- [PULASki: Learning inter-rater variability using statistical distances to improve probabilistic segmentation](https://mlsanity.com/paper/2312.15686/) (2023)
- [Evaluating glioma growth predictions as a forward ranking problem](https://mlsanity.com/paper/2103.11651/) (2021)
- [Multi-Task Diffusion Approach For Prediction of Glioma Tumor Progression](https://mlsanity.com/paper/2509.10824/) (2025)

## Abstract (arXiv)

Existing approaches to modeling the dynamics of brain tumor growth, specifically glioma, employ biologically inspired models of cell diffusion, using image data to estimate the associated parameters. In this work, we propose an alternative approach based on recent advances in probabilistic segmentation and representation learning that implicitly learns growth dynamics directly from data without an underlying explicit model. We present evidence that our approach is able to learn a distribution of plausible future tumor appearances conditioned on past observations of the same tumor.