# Uncertainty-Informed Deep Learning Models Enable High-Confidence Predictions for Digital Histopathology

arXiv: https://arxiv.org/abs/2204.04516 · HTML: https://mlsanity.com/paper/2204.04516/

## Key facts

- Cited by 37 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 62 references that are in the map.
- First posted on arXiv 9 Apr 2022.

## Builds on (most cited first, 17 of 17)

- [Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning](https://mlsanity.com/paper/1506.02142/) (2015)
- [Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles](https://mlsanity.com/paper/1612.01474/) (2016)
- [Analyzing and Improving the Image Quality of StyleGAN](https://mlsanity.com/paper/1912.04958/) (2019)
- [UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction](https://mlsanity.com/paper/1802.03426/) (2018)
- [Data Efficient and Weakly Supervised Computational Pathology on Whole Slide Images](https://mlsanity.com/paper/2004.09666/) (2020)
- [Taking A Closer Look at Domain Shift: Category-level Adversaries for Semantics Consistent Domain Adaptation](https://mlsanity.com/paper/1809.09478/) (2018)
- [A Probabilistic U-Net for Segmentation of Ambiguous Images](https://mlsanity.com/paper/1806.05034/) (2018)
- [Aleatoric uncertainty estimation with test-time augmentation for medical image segmentation with convolutional neural networks](https://mlsanity.com/paper/1807.07356/) (2018)
- [Attacks Which Do Not Kill Training Make Adversarial Learning Stronger](https://mlsanity.com/paper/2002.11242/) (2020)
- [Hyperparameter Ensembles for Robustness and Uncertainty Quantification](https://mlsanity.com/paper/2006.13570/) (2020)
- [MILD-Net: Minimal Information Loss Dilated Network for Gland Instance Segmentation in Colon Histology Images](https://mlsanity.com/paper/1806.01963/) (2018)
- [Automated Gleason Grading of Prostate Biopsies using Deep Learning](https://mlsanity.com/paper/1907.07980/) (2019)
- [Development and Evaluation of a Deep Neural Network for Histologic Classification of Renal Cell Carcinoma on Biopsy and Surgical Resection Slides](https://mlsanity.com/paper/2010.16380/) (2020)
- [Covariate Shift in High-Dimensional Random Feature Regression](https://mlsanity.com/paper/2111.08234/) (2021)
- [MCUa: Multi-level Context and Uncertainty aware Dynamic Deep Ensemble for Breast Cancer Histology Image Classification](https://mlsanity.com/paper/2108.10709/) (2021)
- [Generalisation effects of predictive uncertainty estimation in deep learning for digital pathology](https://mlsanity.com/paper/2112.09693/) (2021)
- [Deep Learning with Uncertainty Quantification for Predicting the Segmentation Dice Coefficient of Prostate Cancer Biopsy Images](https://mlsanity.com/paper/2109.00115/) (2021)

## Built on by (most cited first, 30 of 37)

- [Artificial Intelligence for Digital and Computational Pathology](https://mlsanity.com/paper/2401.06148/) (2023)
- [Trustworthy clinical AI solutions: a unified review of uncertainty quantification in deep learning models for medical image analysis](https://mlsanity.com/paper/2210.03736/) (2022)
- [A Review of Uncertainty Estimation and its Application in Medical Imaging](https://mlsanity.com/paper/2302.08119/) (2023)
- [A review of uncertainty quantification in medical image analysis: probabilistic and non-probabilistic methods](https://mlsanity.com/paper/2310.06873/) (2023)
- [General Place Recognition Survey: Towards Real-World Autonomy](https://mlsanity.com/paper/2405.04812/) (2024)
- [Uncertainty-inspired Open Set Learning for Retinal Anomaly Identification](https://mlsanity.com/paper/2304.03981/) (2023)
- [AI-based Anomaly Detection for Clinical-Grade Histopathological Diagnostics](https://mlsanity.com/paper/2406.14866/) (2024)
- [Slideflow: Deep Learning for Digital Histopathology with Real-Time Whole-Slide Visualization](https://mlsanity.com/paper/2304.04142/) (2023)
- [DPsurv: Dual-Prototype Evidential Fusion for Uncertainty-Aware and Interpretable Whole-Slide Image Survival Prediction](https://mlsanity.com/paper/2510.00053/) (2025)
- [LATTE-MV: Learning to Anticipate Table Tennis Hits from Monocular Videos](https://mlsanity.com/paper/2503.20936/) (2025)
- [Cycle Consistency-based Uncertainty Quantification of Neural Networks in Inverse Imaging Problems](https://mlsanity.com/paper/2305.12852/) (2023)
- [SUDO: a framework for evaluating clinical artificial intelligence systems without ground-truth annotations](https://mlsanity.com/paper/2403.17011/) (2024)
- [Uncertainty-guided annotation enhances segmentation with the human-in-the-loop](https://mlsanity.com/paper/2404.07208/) (2024)
- [FedDBL: Communication and Data Efficient Federated Deep-Broad Learning for Histopathological Tissue Classification](https://mlsanity.com/paper/2302.12662/) (2023)
- [Asymmetric Duos: Sidekicks Improve Uncertainty](https://mlsanity.com/paper/2505.18636/) (2025)
- [ScatterUQ: Interactive Uncertainty Visualizations for Multiclass Deep Learning Problems](https://mlsanity.com/paper/2308.04588/) (2023)
- [Full-ECE: A Metric For Token-level Calibration on Large Language Models](https://mlsanity.com/paper/2406.11345/) (2024)
- [The Disparate Benefits of Deep Ensembles](https://mlsanity.com/paper/2410.13831/) (2024)
- [Simple and Efficient Confidence Score for Grading Whole Slide Images](https://mlsanity.com/paper/2303.04604/) (2023)
- [Leveraging Uncertainty Estimates To Improve Classifier Performance](https://mlsanity.com/paper/2311.11723/) (2023)
- [DeepGleason: a System for Automated Gleason Grading of Prostate Cancer using Deep Neural Networks](https://mlsanity.com/paper/2403.16678/) (2024)
- [Conditional Shift-Robust Conformal Prediction for Graph Neural Network](https://mlsanity.com/paper/2405.11968/) (2024)
- [Measuring Prediction Uncertainty in Neural Cellular Automata](https://mlsanity.com/paper/2605.26726/) (2026)
- [Uncertainty estimation for out-of-distribution detection in computational histopathology](https://mlsanity.com/paper/2210.09909/) (2022)
- [Contrastive Deep Encoding Enables Uncertainty-aware Machine-learning-assisted Histopathology](https://mlsanity.com/paper/2309.07113/) (2023)
- [Evaluation of Predictive Reliability to Foster Trust in Artificial Intelligence. A case study in Multiple Sclerosis](https://mlsanity.com/paper/2402.17554/) (2024)
- [Accurate and Reliable Predictions with Mutual-Transport Ensemble](https://mlsanity.com/paper/2405.19656/) (2024)
- [A Trilogy of AI Safety Frameworks: Paths from Facts and Knowledge Gaps to Reliable Predictions and New Knowledge](https://mlsanity.com/paper/2410.06946/) (2024)
- [Implementing Trust in Non-Small Cell Lung Cancer Diagnosis with a Conformalized Uncertainty-Aware AI Framework in Whole-Slide Images](https://mlsanity.com/paper/2501.00053/) (2024)
- [Reliable Radiologic Skeletal Muscle Area Assessment -- A Biomarker for Cancer Cachexia Diagnosis](https://mlsanity.com/paper/2503.16556/) (2025)

## Abstract (arXiv)

A model's ability to express its own predictive uncertainty is an essential attribute for maintaining clinical user confidence as computational biomarkers are deployed into real-world medical settings. In the domain of cancer digital histopathology, we describe a novel, clinically-oriented approach to uncertainty quantification (UQ) for whole-slide images, estimating uncertainty using dropout and calculating thresholds on training data to establish cutoffs for low- and high-confidence predictions. We train models to identify lung adenocarcinoma vs. squamous cell carcinoma and show that high-confidence predictions outperform predictions without UQ, in both cross-validation and testing on two large external datasets spanning multiple institutions. Our testing strategy closely approximates real-world application, with predictions generated on unsupervised, unannotated slides using predetermined thresholds. Furthermore, we show that UQ thresholding remains reliable in the setting of domain shift, with accurate high-confidence predictions of adenocarcinoma vs. squamous cell carcinoma for out-of-distribution, non-lung cancer cohorts.