# WindowNet: Learnable Windows for Chest X-ray Classification

arXiv: https://arxiv.org/abs/2306.06038 · HTML: https://mlsanity.com/paper/2306.06038/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 22 references that are in the map.
- First posted on arXiv 9 Jun 2023.

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

- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification](https://mlsanity.com/paper/1502.01852/) (2015)
- [CheXpert: A Large Chest Radiograph Dataset with Uncertainty Labels and Expert Comparison](https://mlsanity.com/paper/1901.07031/) (2019)
- [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)
- [CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning](https://mlsanity.com/paper/1711.05225/) (2017)
- [Delving into Masked Autoencoders for Multi-Label Thorax Disease Classification](https://mlsanity.com/paper/2210.12843/) (2022)
- [Attention-based Saliency Maps Improve Interpretability of Pneumothorax Classification](https://mlsanity.com/paper/2303.01871/) (2023)
- [Practical Window Setting Optimization for Medical Image Deep Learning](https://mlsanity.com/paper/1812.00572/) (2018)
- [Stochastic tissue window normalization of deep learning on computed tomography](https://mlsanity.com/paper/1912.00420/) (2019)
- [A knee cannot have lung disease: out-of-distribution detection with in-distribution voting using the medical example of chest X-ray classification](https://mlsanity.com/paper/2208.01077/) (2022)

## Built on by (most cited first, 1 of 1)

- [Interpretable Auto Window Setting for Deep-Learning-Based CT Analysis](https://mlsanity.com/paper/2501.06223/) (2025)

## Abstract (arXiv)

Chest X-ray (CXR) images are commonly compressed to a lower resolution and bit depth to reduce their size, potentially altering subtle diagnostic features. Radiologists use windowing operations to enhance image contrast, but the impact of such operations on CXR classification performance is unclear. In this study, we show that windowing can improve CXR classification performance, and propose WindowNet, a model that learns optimal window settings. We first investigate the impact of bit-depth on classification performance and find that a higher bit-depth (12-bit) leads to improved performance. We then evaluate different windowing settings and show that training with a distinct window generally improves pathology-wise classification performance. Finally, we propose and evaluate WindowNet, a model that learns optimal window settings, and show that it significantly improves performance compared to the baseline model without windowing.