# Uncertainty-aware INVASE: Enhanced Breast Cancer Diagnosis Feature Selection

arXiv: https://arxiv.org/abs/2105.02693 · HTML: https://mlsanity.com/paper/2105.02693/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 18 references that are in the map.
- First posted on arXiv 4 May 2021.

## Builds on (most cited first, 9 of 9)

- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?](https://mlsanity.com/paper/1703.04977/) (2017)
- [Learning Important Features Through Propagating Activation Differences](https://mlsanity.com/paper/1704.02685/) (2017)
- [Learning to Explain: An Information-Theoretic Perspective on Model Interpretation](https://mlsanity.com/paper/1802.07814/) (2018)
- [Bounding Box Regression with Uncertainty for Accurate Object Detection](https://mlsanity.com/paper/1809.08545/) (2018)
- [What Clinicians Want: Contextualizing Explainable Machine Learning for Clinical End Use](https://mlsanity.com/paper/1905.05134/) (2019)
- [Deep Learning for Identifying Metastatic Breast Cancer](https://mlsanity.com/paper/1606.05718/) (2016)
- [Sampling-free Epistemic Uncertainty Estimation Using Approximated Variance Propagation](https://mlsanity.com/paper/1908.00598/) (2019)
- [Bayesian Recurrent Neural Network Models for Forecasting and Quantifying Uncertainty in Spatial-Temporal Data](https://mlsanity.com/paper/1711.00636/) (2017)

## Abstract (arXiv)

In this paper, we present an uncertainty-aware INVASE to quantify predictive confidence of healthcare problem. By introducing learnable Gaussian distributions, we lever-age their variances to measure the degree of uncertainty. Based on the vanilla INVASE, two additional modules are proposed, i.e., an uncertainty quantification module in the predictor, and a reward shaping module in the selector. We conduct extensive experiments on UCI-WDBC dataset. Notably, our method eliminates almost all predictive bias with only about 20% queries, while the uncertainty-agnostic counterpart requires nearly 100% queries. The open-source implementation with a detailed tutorial is available at https://github.com/jx-zhong-for-academic-purpose/Uncertainty-aware-INVASE/blob/main/tutorialinvase%2B.ipynb.