# XNet: A convolutional neural network (CNN) implementation for medical X-Ray image segmentation suitable for small datasets

arXiv: https://arxiv.org/abs/1812.00548 · HTML: https://mlsanity.com/paper/1812.00548/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Dec 2018.

## Built on by (most cited first, 9 of 9)

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- [Detecting, Localising and Classifying Polyps from Colonoscopy Videos using Deep Learning](https://mlsanity.com/paper/2101.03285/) (2021)
- [Object-Based Augmentation Improves Quality of Remote Sensing Semantic Segmentation](https://mlsanity.com/paper/2105.05516/) (2021)
- [Deep ensembles in bioimage segmentation](https://mlsanity.com/paper/2112.12955/) (2021)
- [Automated Precision Localization of Peripherally Inserted Central Catheter Tip through Model-Agnostic Multi-Stage Networks](https://mlsanity.com/paper/2206.06730/) (2022)
- [Global and Local Features through Gaussian Mixture Models on Image Semantic Segmentation](https://mlsanity.com/paper/2207.09162/) (2022)
- [Medical Image Analysis for Detection, Treatment and Planning of Disease using Artificial Intelligence Approaches](https://mlsanity.com/paper/2405.11295/) (2024)
- [Classification of Histopathology Slides with Persistent Homology Convolutions](https://mlsanity.com/paper/2507.14378/) (2025)
- [Water Reflection Detection Using Symmetric Attention](https://mlsanity.com/paper/2607.10749/) (2026)

## Abstract (arXiv)

X-Ray image enhancement, along with many other medical image processing applications, requires the segmentation of images into bone, soft tissue, and open beam regions. We apply a machine learning approach to this problem, presenting an end-to-end solution which results in robust and efficient inference. Since medical institutions frequently do not have the resources to process and label the large quantity of X-Ray images usually needed for neural network training, we design an end-to-end solution for small datasets, while achieving state-of-the-art results. Our implementation produces an overall accuracy of 92%, F1 score of 0.92, and an AUC of 0.98, surpassing classical image processing techniques, such as clustering and entropy based methods, while improving upon the output of existing neural networks used for segmentation in non-medical contexts. The code used for this project is available online.