# ImageCHD: A 3D Computed Tomography Image Dataset for Classification of Congenital Heart Disease

arXiv: https://arxiv.org/abs/2101.10799 · HTML: https://mlsanity.com/paper/2101.10799/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 28 references that are in the map.
- First posted on arXiv 26 Jan 2021.

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

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Quantization of Fully Convolutional Networks for Accurate Biomedical Image Segmentation](https://mlsanity.com/paper/1803.04907/) (2018)
- [A two-stage 3D Unet framework for multi-class segmentation on full resolution image](https://mlsanity.com/paper/1804.04341/) (2018)
- [CFUN: Combining Faster R-CNN and U-net Network for Efficient Whole Heart Segmentation](https://mlsanity.com/paper/1812.04914/) (2018)
- [MSU-Net: Multiscale Statistical U-Net for Real-time 3D Cardiac MRI Video Segmentation](https://mlsanity.com/paper/1909.06726/) (2019)
- [Dilated Convolutional Neural Networks for Cardiovascular MR Segmentation in Congenital Heart Disease](https://mlsanity.com/paper/1704.03669/) (2017)
- [SCNN: A General Distribution based Statistical Convolutional Neural Network with Application to Video Object Detection](https://mlsanity.com/paper/1903.07663/) (2019)
- [A Fine-Grain Error Map Prediction and Segmentation Quality Assessment Framework for Whole-Heart Segmentation](https://mlsanity.com/paper/1907.12244/) (2019)
- [Iterative Segmentation from Limited Training Data: Applications to Congenital Heart Disease](https://mlsanity.com/paper/1809.04182/) (2018)

## Built on by (most cited first, 16 of 16)

- [Implicit Neural Representation in Medical Imaging: A Comparative Survey](https://mlsanity.com/paper/2307.16142/) (2023)
- [A persistent homology-based topological loss for CNN-based multi-class segmentation of CMR](https://mlsanity.com/paper/2107.12689/) (2021)
- [SDF4CHD: Generative Modeling of Cardiac Anatomies with Congenital Heart Defects](https://mlsanity.com/paper/2311.00332/) (2023)
- [USF-MAE: Ultrasound Self-Supervised Foundation Model with Masked Autoencoding](https://mlsanity.com/paper/2510.22990/) (2025)
- [Quantization of Deep Neural Networks for Accurate Edge Computing](https://mlsanity.com/paper/2104.12046/) (2021)
- [EchoCP: An Echocardiography Dataset in Contrast Transthoracic Echocardiography for Patent Foramen Ovale Diagnosis](https://mlsanity.com/paper/2105.08267/) (2021)
- [Full-field surrogate modeling of cardiac function encoding geometric variability](https://mlsanity.com/paper/2504.20479/) (2025)
- [CARDIUM: Congenital Anomaly Recognition with Diagnostic Images and Unified Medical records](https://mlsanity.com/paper/2510.15208/) (2025)
- [Multi-Cycle-Consistent Adversarial Networks for Edge Denoising of Computed Tomography Images](https://mlsanity.com/paper/2104.12044/) (2021)
- [Development of A Real-time POCUS Image Quality Assessment and Acquisition Guidance System](https://mlsanity.com/paper/2212.08624/) (2022)
- [RotCAtt-TransUNet++: Novel Deep Neural Network for Sophisticated Cardiac Segmentation](https://mlsanity.com/paper/2409.05280/) (2024)
- [StructDiff: Structure-aware Diffusion Model for 3D Fine-grained Medical Image Synthesis](https://mlsanity.com/paper/2503.09560/) (2025)
- [From Raw Segmentations to Simulation-Ready Cardiac Meshes: An Automated Framework for Anatomical Reconstruction and Virtual Cohort Generation](https://mlsanity.com/paper/2607.02564/) (2026)
- [MCSeg: Pre-training and Fine-tuning Volumetric Pyramid Transformer for Multi-modal Cardiac Image Segmentation](https://mlsanity.com/paper/2608.30371/) (2026)
- [SV-Cine: Diagnosis-Conditioned Segmentation of Single Ventricle Physiology via Generative Data Augmentation](https://mlsanity.com/paper/2609.12997/) (2026)
- [Beyond In-Distribution Metrics: A Systematic Out-of-Distribution Evaluation of Congenital Heart Disease Segmentation](https://mlsanity.com/paper/2609.17068/) (2026)

## Abstract (arXiv)

Congenital heart disease (CHD) is the most common type of birth defect, which occurs 1 in every 110 births in the United States. CHD usually comes with severe variations in heart structure and great artery connections that can be classified into many types. Thus highly specialized domain knowledge and the time-consuming human process is needed to analyze the associated medical images. On the other hand, due to the complexity of CHD and the lack of dataset, little has been explored on the automatic diagnosis (classification) of CHDs. In this paper, we present ImageCHD, the first medical image dataset for CHD classification. ImageCHD contains 110 3D Computed Tomography (CT) images covering most types of CHD, which is of decent size Classification of CHDs requires the identification of large structural changes without any local tissue changes, with limited data. It is an example of a larger class of problems that are quite difficult for current machine-learning-based vision methods to solve. To demonstrate this, we further present a baseline framework for the automatic classification of CHD, based on a state-of-the-art CHD segmentation method. Experimental results show that the baseline framework can only achieve a classification accuracy of 82.0% under a selective prediction scheme with 88.4% coverage, leaving big room for further improvement. We hope that ImageCHD can stimulate further research and lead to innovative and generic solutions that would have an impact in multiple domains. Our dataset is released to the public compared with existing medical imaging datasets.