# MCUa: Multi-level Context and Uncertainty aware Dynamic Deep Ensemble for Breast Cancer Histology Image Classification

arXiv: https://arxiv.org/abs/2108.10709 · HTML: https://mlsanity.com/paper/2108.10709/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 47 references that are in the map.
- First posted on arXiv 24 Aug 2021.

## Builds on (most cited first, 22 of 22)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning](https://mlsanity.com/paper/1506.02142/) (2015)
- [Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting](https://mlsanity.com/paper/1506.04214/) (2015)
- [A Review of Uncertainty Quantification in Deep Learning: Techniques, Applications and Challenges](https://mlsanity.com/paper/2011.06225/) (2020)
- [Dynamic Routing Between Capsules](https://mlsanity.com/paper/1710.09829/) (2017)
- [BACH: Grand Challenge on Breast Cancer Histology Images](https://mlsanity.com/paper/1808.04277/) (2018)
- [MILD-Net: Minimal Information Loss Dilated Network for Gland Instance Segmentation in Colon Histology Images](https://mlsanity.com/paper/1806.01963/) (2018)
- [Going Deeper with Contextual CNN for Hyperspectral Image Classification](https://mlsanity.com/paper/1604.03519/) (2016)
- [Detecting Cancer Metastases on Gigapixel Pathology Images](https://mlsanity.com/paper/1703.02442/) (2017)
- [Learning Deep Context-aware Features over Body and Latent Parts for Person Re-identification](https://mlsanity.com/paper/1710.06555/) (2017)
- [Y-Net: Joint Segmentation and Classification for Diagnosis of Breast Biopsy Images](https://mlsanity.com/paper/1806.01313/) (2018)
- [Improved Trainable Calibration Method for Neural Networks on Medical Imaging Classification](https://mlsanity.com/paper/2009.04057/) (2020)
- [IAUnet: Global Context-Aware Feature Learning for Person Re-Identification](https://mlsanity.com/paper/2009.01035/) (2020)
- [Context-aware stacked convolutional neural networks for classification of breast carcinomas in whole-slide histopathology images](https://mlsanity.com/paper/1705.03678/) (2017)
- [Hierarchical ResNeXt Models for Breast Cancer Histology Image Classification](https://mlsanity.com/paper/1810.09025/) (2018)
- [Improving High Resolution Histology Image Classification with Deep Spatial Fusion Network](https://mlsanity.com/paper/1807.10552/) (2018)
- [Context-Aware Learning using Transferable Features for Classification of Breast Cancer Histology Images](https://mlsanity.com/paper/1803.00386/) (2018)
- [Assessment of Breast Cancer Histology using Densely Connected Convolutional Networks](https://mlsanity.com/paper/1804.04595/) (2018)
- [Multi Scale Curriculum CNN for Context-Aware Breast MRI Malignancy Classification](https://mlsanity.com/paper/1906.06058/) (2019)

## Built on by (most cited first, 12 of 12)

- [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 quantification in medical image analysis: probabilistic and non-probabilistic methods](https://mlsanity.com/paper/2310.06873/) (2023)
- [Uncertainty-Informed Deep Learning Models Enable High-Confidence Predictions for Digital Histopathology](https://mlsanity.com/paper/2204.04516/) (2022)
- [A Survey on Epistemic (Model) Uncertainty in Supervised Learning: Recent Advances and Applications](https://mlsanity.com/paper/2111.01968/) (2021)
- [Deep Learning in Breast Cancer Imaging: A Decade of Progress and Future Directions](https://mlsanity.com/paper/2304.06662/) (2023)
- [Application of Transfer Learning and Ensemble Learning in Image-level Classification for Breast Histopathology](https://mlsanity.com/paper/2204.08311/) (2022)
- [Scribble-based fast weak-supervision and interactive corrections for segmenting whole slide images](https://mlsanity.com/paper/2402.08333/) (2024)
- [Uncertainty estimation for out-of-distribution detection in computational histopathology](https://mlsanity.com/paper/2210.09909/) (2022)
- [OCU-Net: A Novel U-Net Architecture for Enhanced Oral Cancer Segmentation](https://mlsanity.com/paper/2310.02486/) (2023)
- [Beyond Uncertainty Quantification: Learning Uncertainty for Trust-Informed Neural Network Decisions - A Case Study in COVID-19 Classification](https://mlsanity.com/paper/2410.02805/) (2024)
- [Proximity-Based Evidence Retrieval for Uncertainty-Aware Neural Networks](https://mlsanity.com/paper/2509.13338/) (2025)
- [SSMamba: A Self-Supervised Hybrid State Space Model for Pathological Image Classification](https://mlsanity.com/paper/2604.15711/) (2026)

## Abstract (arXiv)

Breast histology image classification is a crucial step in the early diagnosis of breast cancer. In breast pathological diagnosis, Convolutional Neural Networks (CNNs) have demonstrated great success using digitized histology slides. However, tissue classification is still challenging due to the high visual variability of the large-sized digitized samples and the lack of contextual information. In this paper, we propose a novel CNN, called Multi-level Context and Uncertainty aware (MCUa) dynamic deep learning ensemble model.MCUamodel consists of several multi-level context-aware models to learn the spatial dependency between image patches in a layer-wise fashion. It exploits the high sensitivity to the multi-level contextual information using an uncertainty quantification component to accomplish a novel dynamic ensemble model.MCUamodelhas achieved a high accuracy of 98.11% on a breast cancer histology image dataset. Experimental results show the superior effectiveness of the proposed solution compared to the state-of-the-art histology classification models.