# Reliable Breast Cancer Molecular Subtype Prediction based on uncertainty-aware Bayesian Deep Learning by Mammography

arXiv: https://arxiv.org/abs/2412.11953 · HTML: https://mlsanity.com/paper/2412.11953/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 38 references that are in the map.
- First posted on arXiv 16 Dec 2024.

## Builds on (most cited first, 4 of 4)

- [Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning](https://mlsanity.com/paper/1506.02142/) (2015)
- [A Review of Uncertainty Quantification in Deep Learning: Techniques, Applications and Challenges](https://mlsanity.com/paper/2011.06225/) (2020)
- [Hands-on Bayesian Neural Networks -- a Tutorial for Deep Learning Users](https://mlsanity.com/paper/2007.06823/) (2020)
- [Classification of Luminal Subtypes in Full Mammogram Images Using Transfer Learning](https://mlsanity.com/paper/2301.09282/) (2023)

## Built on by (most cited first, 2 of 2)

- [BI-RADS prediction of mammographic masses using uncertainty information extracted from a Bayesian Deep Learning model](https://mlsanity.com/paper/2503.13999/) (2025)
- [A Deep Bayesian Convolutional Spiking Neural Network-based CAD system with Uncertainty Quantification for Medical Images Classification](https://mlsanity.com/paper/2504.17819/) (2025)

## Abstract (arXiv)

Breast cancer is a heterogeneous disease with different molecular subtypes, clinical behavior, treatment responses as well as survival outcomes. The development of a reliable, accurate, available and inexpensive method to predict the molecular subtypes using medical images plays an important role in the diagnosis and prognosis of breast cancer. Recently, deep learning methods have shown good performance in the breast cancer classification tasks using various medical images. Despite all that success, classical deep learning cannot deliver the predictive uncertainty. The uncertainty represents the validity of the predictions. Therefore, the high predicted uncertainty might cause a negative effect in the accurate diagnosis of breast cancer molecular subtypes. To overcome this, uncertainty quantification methods are used to determine the predictive uncertainty. Accordingly, in this study, we proposed an uncertainty-aware Bayesian deep learning model using the full mammogram images. In addition, to increase the performance of the multi-class molecular subtype classification task, we proposed a novel hierarchical classification strategy, named the two-stage classification strategy. The separate AUC of the proposed model for each subtype was 0.71, 0.75 and 0.86 for HER2-enriched, luminal and triple-negative classes, respectively. The proposed model not only has a comparable performance to other studies in the field of breast cancer molecular subtypes prediction, even using full mammography images, but it is also more reliable, due to quantify the predictive uncertainty.