# MCU-Net: A framework towards uncertainty representations for decision support system patient referrals in healthcare contexts

arXiv: https://arxiv.org/abs/2007.03995 · HTML: https://mlsanity.com/paper/2007.03995/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Jul 2020.

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

- [Data-IQ: Characterizing subgroups with heterogeneous outcomes in tabular data](https://mlsanity.com/paper/2210.13043/) (2022)
- [Enhanced Uncertainty Estimation in Ultrasound Image Segmentation with MSU-Net](https://mlsanity.com/paper/2407.21273/) (2024)
- [Bayesian Neural Networks for 2D MRI Segmentation](https://mlsanity.com/paper/2311.14875/) (2023)
- [Uncertainty-Aware Retinal Vessel Segmentation via Ensemble Distillation](https://mlsanity.com/paper/2509.11689/) (2025)

## Abstract (arXiv)

Incorporating a human-in-the-loop system when deploying automated decision support is critical in healthcare contexts to create trust, as well as provide reliable performance on a patient-to-patient basis. Deep learning methods while having high performance, do not allow for this patient-centered approach due to the lack of uncertainty representation. Thus, we present a framework of uncertainty representation evaluated for medical image segmentation, using MCU-Net which combines a U-Net with Monte Carlo Dropout, evaluated with four different uncertainty metrics. The framework augments this by adding a human-in-the-loop aspect based on an uncertainty threshold for automated referral of uncertain cases to a medical professional. We demonstrate that MCU-Net combined with epistemic uncertainty and an uncertainty threshold tuned for this application maximizes automated performance on an individual patient level, yet refers truly uncertain cases. This is a step towards uncertainty representations when deploying machine learning based decision support in healthcare settings.