# CASED: Curriculum Adaptive Sampling for Extreme Data Imbalance

arXiv: https://arxiv.org/abs/1807.10819 · HTML: https://mlsanity.com/paper/1807.10819/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Jul 2018.

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

- [Curriculum Learning: A Survey](https://mlsanity.com/paper/2101.10382/) (2021)
- [Adaptive Data Optimization: Dynamic Sample Selection with Scaling Laws](https://mlsanity.com/paper/2410.11820/) (2024)
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- [Medical Image Segmentation with Limited Supervision: A Review of Deep Network Models](https://mlsanity.com/paper/2103.00429/) (2021)
- [Surpassing the Human Accuracy: Detecting Gallbladder Cancer from USG Images with Curriculum Learning](https://mlsanity.com/paper/2204.11433/) (2022)
- [A Comprehensive Survey on Imbalanced Data Learning](https://mlsanity.com/paper/2502.08960/) (2025)
- [Stochastic Re-weighted Gradient Descent via Distributionally Robust Optimization](https://mlsanity.com/paper/2306.09222/) (2023)
- [Toddler-Guidance Learning: Impacts of Critical Period on Multimodal AI Agents](https://mlsanity.com/paper/2201.04990/) (2022)
- [CRISP: Curriculum based Sequential Neural Decoders for Polar Code Family](https://mlsanity.com/paper/2210.00313/) (2022)
- [Progressive Growing of Patch Size: Resource-Efficient Curriculum Learning for Dense Prediction Tasks](https://mlsanity.com/paper/2407.07853/) (2024)
- [AutoSampling: Search for Effective Data Sampling Schedules](https://mlsanity.com/paper/2105.13695/) (2021)
- [CLOG-CD: Curriculum Learning based on Oscillating Granularity of Class Decomposed Medical Image Classification](https://mlsanity.com/paper/2505.01741/) (2025)
- [CLPSTNet: A Progressive Multi-Scale Convolutional Steganography Model Integrating Curriculum Learning](https://mlsanity.com/paper/2504.16364/) (2025)
- [Progressive Growing of Patch Size: Curriculum Learning for Accelerated and Improved Medical Image Segmentation](https://mlsanity.com/paper/2510.23241/) (2025)
- [Investigate the Essence of Long-Tailed Recognition from a Unified Perspective](https://mlsanity.com/paper/2107.03758/) (2021)
- [CURVETE: Curriculum Learning and Progressive Self-supervised Training for Medical Image Classification](https://mlsanity.com/paper/2510.23442/) (2025)

## Abstract (arXiv)

We introduce CASED, a novel curriculum sampling algorithm that facilitates the optimization of deep learning segmentation or detection models on data sets with extreme class imbalance. We evaluate the CASED learning framework on the task of lung nodule detection in chest CT. In contrast to two-stage solutions, wherein nodule candidates are first proposed by a segmentation model and refined by a second detection stage, CASED improves the training of deep nodule segmentation models (e.g. UNet) to the point where state of the art results are achieved using only a trivial detection stage. CASED improves the optimization of deep segmentation models by allowing them to first learn how to distinguish nodules from their immediate surroundings, while continuously adding a greater proportion of difficult-to-classify global context, until uniformly sampling from the empirical data distribution. Using CASED during training yields a minimalist proposal to the lung nodule detection problem that tops the LUNA16 nodule detection benchmark with an average sensitivity score of 88.35%. Furthermore, we find that models trained using CASED are robust to nodule annotation quality by showing that comparable results can be achieved when only a point and radius for each ground truth nodule are provided during training. Finally, the CASED learning framework makes no assumptions with regard to imaging modality or segmentation target and should generalize to other medical imaging problems where class imbalance is a persistent problem.