# TumorCP: A Simple but Effective Object-Level Data Augmentation for Tumor Segmentation

arXiv: https://arxiv.org/abs/2107.09843 · HTML: https://mlsanity.com/paper/2107.09843/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 19 references that are in the map.
- First posted on arXiv 21 Jul 2021.

## Builds on (most cited first, 15 of 15)

- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [mixup: Beyond Empirical Risk Minimization](https://mlsanity.com/paper/1710.09412/) (2017)
- [FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence](https://mlsanity.com/paper/2001.07685/) (2020)
- [Self-training with Noisy Student improves ImageNet classification](https://mlsanity.com/paper/1911.04252/) (2019)
- [Simple Copy-Paste is a Strong Data Augmentation Method for Instance Segmentation](https://mlsanity.com/paper/2012.07177/) (2020)
- [Cut, Paste and Learn: Surprisingly Easy Synthesis for Instance Detection](https://mlsanity.com/paper/1708.01642/) (2017)
- [Representation Learning via Invariant Causal Mechanisms](https://mlsanity.com/paper/2010.07922/) (2020)
- [The KiTS19 Challenge Data: 300 Kidney Tumor Cases with Clinical Context, CT Semantic Segmentations, and Surgical Outcomes](https://mlsanity.com/paper/1904.00445/) (2019)
- [Models Genesis: Generic Autodidactic Models for 3D Medical Image Analysis](https://mlsanity.com/paper/1908.06912/) (2019)
- [Medical Image Synthesis for Data Augmentation and Anonymization using Generative Adversarial Networks](https://mlsanity.com/paper/1807.10225/) (2018)
- [InstaBoost: Boosting Instance Segmentation via Probability Map Guided Copy-Pasting](https://mlsanity.com/paper/1908.07801/) (2019)
- [Synthetic Augmentation and Feature-based Filtering for Improved Cervical Histopathology Image Classification](https://mlsanity.com/paper/1907.10655/) (2019)
- [CompareNet: Anatomical Segmentation Network with Deep Non-local Label Fusion](https://mlsanity.com/paper/1910.04797/) (2019)
- [OralViewer: 3D Demonstration of Dental Surgeries for Patient Education with Oral Cavity Reconstruction from a 2D Panoramic X-ray](https://mlsanity.com/paper/2101.00098/) (2020)
- [Atlas-aware ConvNetfor Accurate yet Robust Anatomical Segmentation](https://mlsanity.com/paper/2102.01256/) (2021)

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

- [Data efficient deep learning for medical image analysis: A survey](https://mlsanity.com/paper/2310.06557/) (2023)
- [Domain-guided data augmentation for deep learning on medical imaging](https://mlsanity.com/paper/2210.04977/) (2022)
- [Cut-Paste Consistency Learning for Semi-Supervised Lesion Segmentation](https://mlsanity.com/paper/2210.00191/) (2022)
- [Soft-CP: A Credible and Effective Data Augmentation for Semantic Segmentation of Medical Lesions](https://mlsanity.com/paper/2203.10507/) (2022)
- [MSRepaint: Multiple Sclerosis Repaint with Conditional Denoising Diffusion Implicit Model for Bidirectional Lesion Filling and Synthesis](https://mlsanity.com/paper/2510.02063/) (2025)
- [MIPR:Automatic Annotation of Medical Images with Pixel Rearrangement](https://mlsanity.com/paper/2204.10513/) (2022)
- [Context-Matched Collage Generation for Underwater Invertebrate Detection](https://mlsanity.com/paper/2211.08479/) (2022)
- [Learning Transferable Object-Centric Diffeomorphic Transformations for Data Augmentation in Medical Image Segmentation](https://mlsanity.com/paper/2307.13645/) (2023)
- [CP-Dilatation: A Copy-and-Paste Augmentation Method for Preserving the Boundary Context Information of Histopathology Images](https://mlsanity.com/paper/2507.04660/) (2025)

## Abstract (arXiv)

Deep learning models are notoriously data-hungry. Thus, there is an urging need for data-efficient techniques in medical image analysis, where well-annotated data are costly and time consuming to collect. Motivated by the recently revived "Copy-Paste" augmentation, we propose TumorCP, a simple but effective object-level data augmentation method tailored for tumor segmentation. TumorCP is online and stochastic, providing unlimited augmentation possibilities for tumors' subjects, locations, appearances, as well as morphologies. Experiments on kidney tumor segmentation task demonstrate that TumorCP surpasses the strong baseline by a remarkable margin of 7.12% on tumor Dice. Moreover, together with image-level data augmentation, it beats the current state-of-the-art by 2.32% on tumor Dice. Comprehensive ablation studies are performed to validate the effectiveness of TumorCP. Meanwhile, we show that TumorCP can lead to striking improvements in extremely low-data regimes. Evaluated with only 10% labeled data, TumorCP significantly boosts tumor Dice by 21.87%. To the best of our knowledge, this is the very first work exploring and extending the "Copy-Paste" design in medical imaging domain. Code is available at: https://github.com/YaoZhang93/TumorCP.