# Refining Tuberculosis Detection in CXR Imaging: Addressing Bias in Deep Neural Networks via Interpretability

arXiv: https://arxiv.org/abs/2407.14064 · HTML: https://mlsanity.com/paper/2407.14064/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 24 references that are in the map.
- First posted on arXiv 19 Jul 2024.

## Builds on (most cited first, 10 of 10)

- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [ChestX-ray8: Hospital-scale Chest X-ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases](https://mlsanity.com/paper/1705.02315/) (2017)
- [The Mythos of Model Interpretability](https://mlsanity.com/paper/1606.03490/) (2016)
- [Score-CAM: Score-Weighted Visual Explanations for Convolutional Neural Networks](https://mlsanity.com/paper/1910.01279/) (2019)
- [Explaining Explanations: An Overview of Interpretability of Machine Learning](https://mlsanity.com/paper/1806.00069/) (2018)
- [Use HiResCAM instead of Grad-CAM for faithful explanations of convolutional neural networks](https://mlsanity.com/paper/2011.08891/) (2020)
- [MOON: A Mixed Objective Optimization Network for the Recognition of Facial Attributes](https://mlsanity.com/paper/1603.07027/) (2016)
- [Revisiting Computer-Aided Tuberculosis Diagnosis](https://mlsanity.com/paper/2307.02848/) (2023)
- [Biased Binary Attribute Classifiers Ignore the Majority Classes](https://mlsanity.com/paper/2403.14435/) (2024)

## Abstract (arXiv)

Automatic classification of active tuberculosis from chest X-ray images has the potential to save lives, especially in low- and mid-income countries where skilled human experts can be scarce. Given the lack of available labeled data to train such systems and the unbalanced nature of publicly available datasets, we argue that the reliability of deep learning models is limited, even if they can be shown to obtain perfect classification accuracy on the test data. One way of evaluating the reliability of such systems is to ensure that models use the same regions of input images for predictions as medical experts would. In this paper, we show that pre-training a deep neural network on a large-scale proxy task, as well as using mixed objective optimization network (MOON), a technique to balance different classes during pre-training and fine-tuning, can improve the alignment of decision foundations between models and experts, as compared to a model directly trained on the target dataset. At the same time, these approaches keep perfect classification accuracy according to the area under the receiver operating characteristic curve (AUROC) on the test set, and improve generalization on an independent, unseen dataset. For the purpose of reproducibility, our source code is made available online.