mlsanityData to 7 Oct 2026

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

arXiv 2108.10709 ↗First posted 24 Aug 2021cs.CV cs.AIcited by 12

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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.

Key facts. Cited by 12 papers in the map (data through 7 Oct 2026). Builds on 22 of its 47 references that are in the map. First posted on arXiv 24 Aug 2021.

Builds on

22 of 47 references.

References · 22

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10 more
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 1; 2022: 4; 2023: 3; 2024: 2; 2025: 1; 2026: 1.

12 papers.

  1. 2022Trustworthy clinical AI solutions: a unified review of uncertainty quantification in deep learning models for medical image analysiscited by 62
  2. 2023A review of uncertainty quantification in medical image analysis: probabilistic and non-probabilistic methodscited by 39
  3. 2022Uncertainty-Informed Deep Learning Models Enable High-Confidence Predictions for Digital Histopathologycited by 37
  4. 2021A Survey on Epistemic (Model) Uncertainty in Supervised Learning: Recent Advances and Applicationscited by 24
  5. 2023Deep Learning in Breast Cancer Imaging: A Decade of Progress and Future Directionscited by 10
  6. 2022Application of Transfer Learning and Ensemble Learning in Image-level Classification for Breast Histopathologycited by 6
  7. 2024Scribble-based fast weak-supervision and interactive corrections for segmenting whole slide imagescited by 1
  8. 2022Uncertainty estimation for out-of-distribution detection in computational histopathologycited by 0
  9. 2023OCU-Net: A Novel U-Net Architecture for Enhanced Oral Cancer Segmentationcited by 0
  10. 2024Beyond Uncertainty Quantification: Learning Uncertainty for Trust-Informed Neural Network Decisions - A Case Study in COVID-19 Classificationcited by 0
  11. 2025Proximity-Based Evidence Retrieval for Uncertainty-Aware Neural Networkscited by 0
  12. 2026SSMamba: A Self-Supervised Hybrid State Space Model for Pathological Image Classificationcited by 0