# Synthesizing Diverse Lung Nodules Wherever Massively: 3D Multi-Conditional GAN-based CT Image Augmentation for Object Detection

arXiv: https://arxiv.org/abs/1906.04962 · HTML: https://mlsanity.com/paper/1906.04962/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Jun 2019.

## Built on by (most cited first, 29 of 29)

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- [FreeTumor: Advance Tumor Segmentation via Large-Scale Tumor Synthesis](https://mlsanity.com/paper/2406.01264/) (2024)
- [Synthetic Tumors Make AI Segment Tumors Better](https://mlsanity.com/paper/2210.14845/) (2022)
- [Generative Enhancement for 3D Medical Images](https://mlsanity.com/paper/2403.12852/) (2024)
- [FakeNews: GAN-based generation of realistic 3D volumetric data -- A systematic review and taxonomy](https://mlsanity.com/paper/2207.01390/) (2022)
- [Image Synthesis with Disentangled Attributes for Chest X-Ray Nodule Augmentation and Detection](https://mlsanity.com/paper/2207.09389/) (2022)
- [Adversarial Fine-tuning using Generated Respiratory Sound to Address Class Imbalance](https://mlsanity.com/paper/2311.06480/) (2023)
- [Less is More: Unsupervised Mask-guided Annotated CT Image Synthesis with Minimum Manual Segmentations](https://mlsanity.com/paper/2303.12747/) (2023)
- [Breast Cancer Immunohistochemical Image Generation: a Benchmark Dataset and Challenge Review](https://mlsanity.com/paper/2305.03546/) (2023)
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- [Confidence-Aware and Self-Supervised Image Anomaly Localisation](https://mlsanity.com/paper/2303.13227/) (2023)
- [A systematic study of the foreground-background imbalance problem in deep learning for object detection](https://mlsanity.com/paper/2306.16539/) (2023)
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- [Supervised Segmentation with Domain Adaptation for Small Sampled Orbital CT Images](https://mlsanity.com/paper/2107.00418/) (2021)
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- [Decoupling Shape and Density for Liver Lesion Synthesis Using Conditional Generative Adversarial Networks](https://mlsanity.com/paper/2106.00629/) (2021)
- [Tips and Tricks to Improve CNN-based Chest X-ray Diagnosis: A Survey](https://mlsanity.com/paper/2106.00997/) (2021)
- [Comparative clinical evaluation of "memory-efficient" synthetic 3d generative adversarial networks (gan) head-to-head to state of art: results on computed tomography of the chest](https://mlsanity.com/paper/2501.15572/) (2025)
- [CAFusion: Controllable Anatomical Synthesis of Perirectal Lymph Nodes via SDF-guided Diffusion](https://mlsanity.com/paper/2503.06919/) (2025)
- [Lung Nodule Image Synthesis Driven by Two-Stage Generative Adversarial Networks](https://mlsanity.com/paper/2602.02171/) (2026)
- [HUydra: Full-Range Lung CT Synthesis via Multiple HU Interval Generative Modelling](https://mlsanity.com/paper/2603.23041/) (2026)
- [Controllable Lung Nodule Synthesis via Histogram-Regularized Latent Diffusion Models](https://mlsanity.com/paper/2605.30631/) (2026)

## Abstract (arXiv)

Accurate Computer-Assisted Diagnosis, relying on large-scale annotated pathological images, can alleviate the risk of overlooking the diagnosis. Unfortunately, in medical imaging, most available datasets are small/fragmented. To tackle this, as a Data Augmentation (DA) method, 3D conditional Generative Adversarial Networks (GANs) can synthesize desired realistic/diverse 3D images as additional training data. However, no 3D conditional GAN-based DA approach exists for general bounding box-based 3D object detection, while it can locate disease areas with physicians' minimum annotation cost, unlike rigorous 3D segmentation. Moreover, since lesions vary in position/size/attenuation, further GAN-based DA performance requires multiple conditions. Therefore, we propose 3D Multi-Conditional GAN (MCGAN) to generate realistic/diverse 32 X 32 X 32 nodules placed naturally on lung Computed Tomography images to boost sensitivity in 3D object detection. Our MCGAN adopts two discriminators for conditioning: the context discriminator learns to classify real vs synthetic nodule/surrounding pairs with noise box-centered surroundings; the nodule discriminator attempts to classify real vs synthetic nodules with size/attenuation conditions. The results show that 3D Convolutional Neural Network-based detection can achieve higher sensitivity under any nodule size/attenuation at fixed False Positive rates and overcome the medical data paucity with the MCGAN-generated realistic nodules---even expert physicians fail to distinguish them from the real ones in Visual Turing Test.