# Analyzing Tumors by Synthesis

arXiv: https://arxiv.org/abs/2409.06035 · HTML: https://mlsanity.com/paper/2409.06035/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 48 of its 121 references that are in the map.
- First posted on arXiv 9 Sep 2024.

## Builds on (most cited first, 30 of 48)

- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [Hierarchical Text-Conditional Image Generation with CLIP Latents](https://mlsanity.com/paper/2204.06125/) (2022)
- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- [Towards Total Recall in Industrial Anomaly Detection](https://mlsanity.com/paper/2106.08265/) (2021)
- [The Medical Segmentation Decathlon](https://mlsanity.com/paper/2106.05735/) (2021)
- [The Liver Tumor Segmentation Benchmark (LiTS)](https://mlsanity.com/paper/1901.04056/) (2019)
- [Rotation Equivariant CNNs for Digital Pathology](https://mlsanity.com/paper/1806.03962/) (2018)
- [CLIP-Driven Universal Model for Organ Segmentation and Tumor Detection](https://mlsanity.com/paper/2301.00785/) (2023)
- [Validation, comparison, and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images: the LUNA16 challenge](https://mlsanity.com/paper/1612.08012/) (2016)
- [Unsupervised Medical Image Translation with Adversarial Diffusion Models](https://mlsanity.com/paper/2207.08208/) (2022)
- [The state of the art in kidney and kidney tumor segmentation in contrast-enhanced CT imaging: Results of the KiTS19 Challenge](https://mlsanity.com/paper/1912.01054/) (2019)
- [Models Genesis: Generic Autodidactic Models for 3D Medical Image Analysis](https://mlsanity.com/paper/1908.06912/) (2019)
- [Autoencoders for Unsupervised Anomaly Segmentation in Brain MR Images: A Comparative Study](https://mlsanity.com/paper/2004.03271/) (2020)
- [Standardized Assessment of Automatic Segmentation of White Matter Hyperintensities and Results of the WMH Segmentation Challenge](https://mlsanity.com/paper/1904.00682/) (2019)
- [COVID-CT-Dataset: A CT Scan Dataset about COVID-19](https://mlsanity.com/paper/2003.13865/) (2020)
- [Towards Generalizable Tumor Synthesis](https://mlsanity.com/paper/2402.19470/) (2024)
- [AbdomenAtlas: A Large-Scale, Detailed-Annotated, & Multi-Center Dataset for Efficient Transfer Learning and Open Algorithmic Benchmarking](https://mlsanity.com/paper/2407.16697/) (2024)
- [CovidGAN: Data Augmentation Using Auxiliary Classifier GAN for Improved Covid-19 Detection](https://mlsanity.com/paper/2103.05094/) (2021)
- [Label-Free Liver Tumor Segmentation](https://mlsanity.com/paper/2303.14869/) (2023)
- [Endora: Video Generation Models as Endoscopy Simulators](https://mlsanity.com/paper/2403.11050/) (2024)
- [AbdomenAtlas-8K: Annotating 8,000 CT Volumes for Multi-Organ Segmentation in Three Weeks](https://mlsanity.com/paper/2305.09666/) (2023)
- [Continual Learning for Abdominal Multi-Organ and Tumor Segmentation](https://mlsanity.com/paper/2306.00988/) (2023)
- [Deflating Dataset Bias Using Synthetic Data Augmentation](https://mlsanity.com/paper/2004.13866/) (2020)
- [Label-Free Segmentation of COVID-19 Lesions in Lung CT](https://mlsanity.com/paper/2009.06456/) (2020)
- [Learning Semantics-enriched Representation via Self-discovery, Self-classification, and Self-restoration](https://mlsanity.com/paper/2007.06959/) (2020)
- [Selective Synthetic Augmentation with HistoGAN for Improved Histopathology Image Classification](https://mlsanity.com/paper/2111.06399/) (2021)
- [CT-Realistic Lung Nodule Simulation from 3D Conditional Generative Adversarial Networks for Robust Lung Segmentation](https://mlsanity.com/paper/1806.04051/) (2018)
- [Universal and Extensible Language-Vision Models for Organ Segmentation and Tumor Detection from Abdominal Computed Tomography](https://mlsanity.com/paper/2405.18356/) (2024)
- [COVID-19 CT Image Synthesis with a Conditional Generative Adversarial Network](https://mlsanity.com/paper/2007.14638/) (2020)
- [DiffInfinite: Large Mask-Image Synthesis via Parallel Random Patch Diffusion in Histopathology](https://mlsanity.com/paper/2306.13384/) (2023)

## Built on by (most cited first, 11 of 11)

- [RadGPT: Constructing 3D Image-Text Tumor Datasets](https://mlsanity.com/paper/2501.04678/) (2025)
- [Touchstone Benchmark: Are We on the Right Way for Evaluating AI Algorithms for Medical Segmentation?](https://mlsanity.com/paper/2411.03670/) (2024)
- [Text-Driven Tumor Synthesis](https://mlsanity.com/paper/2412.18589/) (2024)
- [MedSegFactory: Text-Guided Generation of Medical Image-Mask Pairs](https://mlsanity.com/paper/2504.06897/) (2025)
- [Scaling Tumor Segmentation: Best Lessons from Real and Synthetic Data](https://mlsanity.com/paper/2510.14831/) (2025)
- [Learning Patient-Specific Disease Dynamics with Latent Flow Matching for Longitudinal Imaging Generation](https://mlsanity.com/paper/2512.09185/) (2025)
- [See More, Change Less: Anatomy-Aware Diffusion for Contrast Enhancement](https://mlsanity.com/paper/2512.07251/) (2025)
- [Are Video Models Emerging as Zero-Shot Learners and Reasoners in Medical Imaging?](https://mlsanity.com/paper/2510.10254/) (2025)
- [Auditing Significance, Metric Choice, and Demographic Fairness in Medical AI Challenges](https://mlsanity.com/paper/2512.19091/) (2025)
- [Patient-Specific Autoregressive Models for Organ Motion Prediction in Radiotherapy](https://mlsanity.com/paper/2505.11832/) (2025)
- [Foundation VAEs for 3D CT Reconstruction, Augmentation, and Generation](https://mlsanity.com/paper/2605.30893/) (2026)

## Abstract (arXiv)

Computer-aided tumor detection has shown great potential in enhancing the interpretation of over 80 million CT scans performed annually in the United States. However, challenges arise due to the rarity of CT scans with tumors, especially early-stage tumors. Developing AI with real tumor data faces issues of scarcity, annotation difficulty, and low prevalence. Tumor synthesis addresses these challenges by generating numerous tumor examples in medical images, aiding AI training for tumor detection and segmentation. Successful synthesis requires realistic and generalizable synthetic tumors across various organs. This chapter reviews AI development on real and synthetic data and summarizes two key trends in synthetic data for cancer imaging research: modeling-based and learning-based approaches. Modeling-based methods, like Pixel2Cancer, simulate tumor development over time using generic rules, while learning-based methods, like DiffTumor, learn from a few annotated examples in one organ to generate synthetic tumors in others. Reader studies with expert radiologists show that synthetic tumors can be convincingly realistic. We also present case studies in the liver, pancreas, and kidneys reveal that AI trained on synthetic tumors can achieve performance comparable to, or better than, AI only trained on real data. Tumor synthesis holds significant promise for expanding datasets, enhancing AI reliability, improving tumor detection performance, and preserving patient privacy.