mlsanityData to 7 Oct 2026

Synthetic Tumors Make AI Segment Tumors Better

arXiv 2210.14845 ↗First posted 26 Oct 2022eess.IV cs.CV cs.LGcited by 14

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Abstract (arXiv)

We develop a novel strategy to generate synthetic tumors. Unlike existing works, the tumors generated by our strategy have two intriguing advantages: (1) realistic in shape and texture, which even medical professionals can confuse with real tumors; (2) effective for AI model training, which can perform liver tumor segmentation similarly to a model trained on real tumors - this result is unprecedented because no existing work, using synthetic tumors only, has thus far reached a similar or even close performance to the model trained on real tumors. This result also implies that manual efforts for developing per-voxel annotation of tumors (which took years to create) can be considerably reduced for training AI models in the future. Moreover, our synthetic tumors have the potential to improve the success rate of small tumor detection by automatically generating enormous examples of small (or tiny) synthetic tumors.

Key facts. Cited by 14 papers in the map (data through 7 Oct 2026). Builds on 13 of its 23 references that are in the map. First posted on arXiv 26 Oct 2022.

Builds on

13 of 23 references.

References · 13

  1. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 29,915
  2. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
  3. 2021Swin Transformer: Hierarchical Vision Transformer using Shifted Windowscited by 12,082
  4. 2018UNet++: A Nested U-Net Architecture for Medical Image Segmentationcited by 1,518
  5. 2022Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Imagescited by 823
  6. 2019UNet++: Redesigning Skip Connections to Exploit Multiscale Features in Image Segmentationcited by 583
  7. 2019The Liver Tumor Segmentation Benchmark (LiTS)cited by 504
  8. 2021Self-Supervised Pre-Training of Swin Transformers for 3D Medical Image Analysiscited by 418
  9. 2020CHAOS Challenge -- Combined (CT-MR) Healthy Abdominal Organ Segmentationcited by 307
  10. 2020Label-Free Segmentation of COVID-19 Lesions in Lung CTcited by 37
  11. 2019Synthesizing Diverse Lung Nodules Wherever Massively: 3D Multi-Conditional GAN-based CT Image Augmentation for Object Detectioncited by 29
  12. 2021Free-form tumor synthesis in computed tomography images via richer generative adversarial networkcited by 16
1 more
  1. 2021METGAN: Generative Tumour Inpainting and Modality Synthesis in Light Sheet Microscopycited by 9

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 5; 2024: 5; 2025: 3; 2026: 1.

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  1. 2024MAISI: Medical AI for Synthetic Imagingcited by 86
  2. 2024Towards Generalizable Tumor Synthesiscited by 65
  3. 2024AbdomenAtlas: A Large-Scale, Detailed-Annotated, & Multi-Center Dataset for Efficient Transfer Learning and Open Algorithmic Benchmarkingcited by 64
  4. 2023Label-Free Liver Tumor Segmentationcited by 56
  5. 2023AbdomenAtlas-8K: Annotating 8,000 CT Volumes for Multi-Organ Segmentation in Three Weekscited by 52
  6. 2024From Pixel to Cancer: Cellular Automata in Computed Tomographycited by 20
  7. 2023Early Detection and Localization of Pancreatic Cancer by Label-Free Tumor Synthesiscited by 17
  8. 2023Boosting Dermatoscopic Lesion Segmentation via Diffusion Models with Visual and Textual Promptscited by 17
  9. 2025Scaling Tumor Segmentation: Best Lessons from Real and Synthetic Datacited by 13
  10. 2024Analyzing Tumors by Synthesiscited by 11
  11. 2023Synthetic Data as Validationcited by 9
  12. 2025Expectation-Maximization as the Engine of Scalable Medical Intelligencecited by 5
2 more
  1. 2025See More, Change Less: Anatomy-Aware Diffusion for Contrast Enhancementcited by 3
  2. 20263D-LLDM: Label-Guided 3D Latent Diffusion Model for Improving High-Resolution Synthetic MR Imaging in Hepatic Structure Segmentationcited by 2