# COLosSAL: A Benchmark for Cold-start Active Learning for 3D Medical Image Segmentation

arXiv: https://arxiv.org/abs/2307.12004 · HTML: https://mlsanity.com/paper/2307.12004/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 28 references that are in the map.
- First posted on arXiv 22 Jul 2023.

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

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning](https://mlsanity.com/paper/1506.02142/) (2015)
- [The Medical Segmentation Decathlon](https://mlsanity.com/paper/2106.05735/) (2021)
- [MONAI: An open-source framework for deep learning in healthcare](https://mlsanity.com/paper/2211.02701/) (2022)
- [Suggestive Annotation: A Deep Active Learning Framework for Biomedical Image Segmentation](https://mlsanity.com/paper/1706.04737/) (2017)
- [Active Learning on a Budget: Opposite Strategies Suit High and Low Budgets](https://mlsanity.com/paper/2202.02794/) (2022)
- [Cold-start Active Learning through Self-supervised Language Modeling](https://mlsanity.com/paper/2010.09535/) (2020)
- [Diminishing Uncertainty within the Training Pool: Active Learning for Medical Image Segmentation](https://mlsanity.com/paper/2101.02323/) (2021)
- [Rethinking deep active learning: Using unlabeled data at model training](https://mlsanity.com/paper/1911.08177/) (2019)
- [Parting with Illusions about Deep Active Learning](https://mlsanity.com/paper/1912.05361/) (2019)
- [Addressing the Item Cold-start Problem by Attribute-driven Active Learning](https://mlsanity.com/paper/1805.09023/) (2018)
- [Warm Start Active Learning with Proxy Labels & Selection via Semi-Supervised Fine-Tuning](https://mlsanity.com/paper/2209.06285/) (2022)
- [On Initial Pools for Deep Active Learning](https://mlsanity.com/paper/2011.14696/) (2020)
- [TAAL: Test-time Augmentation for Active Learning in Medical Image Segmentation](https://mlsanity.com/paper/2301.06624/) (2023)
- [Self-supervised Assisted Active Learning for Skin Lesion Segmentation](https://mlsanity.com/paper/2205.07021/) (2022)

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

- [A comprehensive survey on deep active learning in medical image analysis](https://mlsanity.com/paper/2310.14230/) (2023)
- [Integrating Deep Metric Learning with Coreset for Active Learning in 3D Segmentation](https://mlsanity.com/paper/2411.15763/) (2024)
- [SAM2-3dMed: Empowering SAM2 for 3D Medical Image Segmentation](https://mlsanity.com/paper/2510.08967/) (2025)
- [SimSAM: Zero-shot Medical Image Segmentation via Simulated Interaction](https://mlsanity.com/paper/2406.00663/) (2024)
- [Finally Outshining the Random Baseline: A Simple and Effective Solution for Active Learning in 3D Biomedical Imaging](https://mlsanity.com/paper/2601.13677/) (2026)
- [From Cold Start to Active Learning: Embedding-Based Scan Selection for Medical Image Segmentation](https://mlsanity.com/paper/2601.18532/) (2026)
- [QuickDraw: Fast Visualization, Analysis and Active Learning for Medical Image Segmentation](https://mlsanity.com/paper/2503.09885/) (2025)
- [Dataset-Aware Cold-Start Active Learning for Annotation-Efficient 3D Medical Image Segmentation](https://mlsanity.com/paper/2606.20765/) (2026)
- [One-Shot Data Selection for Medical Image Classification via Graph Coverage](https://mlsanity.com/paper/2606.22002/) (2026)

## Abstract (arXiv)

Medical image segmentation is a critical task in medical image analysis. In recent years, deep learning based approaches have shown exceptional performance when trained on a fully-annotated dataset. However, data annotation is often a significant bottleneck, especially for 3D medical images. Active learning (AL) is a promising solution for efficient annotation but requires an initial set of labeled samples to start active selection. When the entire data pool is unlabeled, how do we select the samples to annotate as our initial set? This is also known as the cold-start AL, which permits only one chance to request annotations from experts without access to previously annotated data. Cold-start AL is highly relevant in many practical scenarios but has been under-explored, especially for 3D medical segmentation tasks requiring substantial annotation effort. In this paper, we present a benchmark named COLosSAL by evaluating six cold-start AL strategies on five 3D medical image segmentation tasks from the public Medical Segmentation Decathlon collection. We perform a thorough performance analysis and explore important open questions for cold-start AL, such as the impact of budget on different strategies. Our results show that cold-start AL is still an unsolved problem for 3D segmentation tasks but some important trends have been observed. The code repository, data partitions, and baseline results for the complete benchmark are publicly available at https://github.com/MedICL-VU/COLosSAL.