# Uncertainty in multitask learning: joint representations for probabilistic MR-only radiotherapy planning

arXiv: https://arxiv.org/abs/1806.06595 · HTML: https://mlsanity.com/paper/1806.06595/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Jun 2018.

## Built on by (most cited first, 8 of 8)

- [A Review of Uncertainty Estimation and its Application in Medical Imaging](https://mlsanity.com/paper/2302.08119/) (2023)
- [Deep learning-based synthetic-CT generation in radiotherapy and PET: a review](https://mlsanity.com/paper/2102.02734/) (2021)
- [Recalibration of Aleatoric and Epistemic Regression Uncertainty in Medical Imaging](https://mlsanity.com/paper/2104.12376/) (2021)
- [Multi-task Representation Learning for Pure Exploration in Linear Bandits](https://mlsanity.com/paper/2302.04441/) (2023)
- [Multi-task Representation Learning for Pure Exploration in Bilinear Bandits](https://mlsanity.com/paper/2311.00327/) (2023)
- [A Decoupled Uncertainty Model for MRI Segmentation Quality Estimation](https://mlsanity.com/paper/2109.02413/) (2021)
- [Improving Uncertainty-Error Correspondence in Deep Bayesian Medical Image Segmentation](https://mlsanity.com/paper/2409.03470/) (2024)
- [AI-Assisted Decision-Making for Clinical Assessment of Auto-Segmented Contour Quality](https://mlsanity.com/paper/2505.00308/) (2025)

## Abstract (arXiv)

Multi-task neural network architectures provide a mechanism that jointly integrates information from distinct sources. It is ideal in the context of MR-only radiotherapy planning as it can jointly regress a synthetic CT (synCT) scan and segment organs-at-risk (OAR) from MRI. We propose a probabilistic multi-task network that estimates: 1) intrinsic uncertainty through a heteroscedastic noise model for spatially-adaptive task loss weighting and 2) parameter uncertainty through approximate Bayesian inference. This allows sampling of multiple segmentations and synCTs that share their network representation. We test our model on prostate cancer scans and show that it produces more accurate and consistent synCTs with a better estimation in the variance of the errors, state of the art results in OAR segmentation and a methodology for quality assurance in radiotherapy treatment planning.