mlsanityData to 7 Oct 2026

Less Is More: A Comparison of Active Learning Strategies for 3D Medical Image Segmentation

arXiv 2207.00845 ↗First posted 2 Jul 2022cs.CV cs.AI cs.LGcited by 7

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Abstract (arXiv)

Since labeling medical image data is a costly and labor-intensive process, active learning has gained much popularity in the medical image segmentation domain in recent years. A variety of active learning strategies have been proposed in the literature, but their effectiveness is highly dependent on the dataset and training scenario. To facilitate the comparison of existing strategies and provide a baseline for evaluating novel strategies, we evaluate the performance of several well-known active learning strategies on three datasets from the Medical Segmentation Decathlon. Additionally, we consider a strided sampling strategy specifically tailored to 3D image data. We demonstrate that both random and strided sampling act as strong baselines and discuss the advantages and disadvantages of the studied methods. To allow other researchers to compare their work to our results, we provide an open-source framework for benchmarking active learning strategies on a variety of medical segmentation datasets.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 16 of its 31 references that are in the map. First posted on arXiv 2 Jul 2022.

Builds on

16 of 31 references.

References · 16

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4 more
  1. 2018MedAL: Deep Active Learning Sampling Method for Medical Image Analysiscited by 19
  2. 2015Introducing Geometry in Active Learning for Image Segmentationcited by 12
  3. 2019ALiPy: Active Learning in Pythoncited by 11
  4. 2019A sparse annotation strategy based on attention-guided active learning for 3D medical image segmentationcited by 5

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 2; 2024: 4; 2025: 0; 2026: 1.

7 papers.

  1. 2023Active learning for medical image segmentation with stochastic batchescited by 20
  2. 2024Breaking the Barrier: Selective Uncertainty-based Active Learning for Medical Image Segmentationcited by 9
  3. 2024Integrating Deep Metric Learning with Coreset for Active Learning in 3D Segmentationcited by 7
  4. 2024A Review of Bayesian Uncertainty Quantification in Deep Probabilistic Image Segmentationcited by 6
  5. 2023Exploring UMAP in hybrid models of entropy-based and representativeness sampling for active learning in biomedical segmentationcited by 1
  6. 2026Finally Outshining the Random Baseline: A Simple and Effective Solution for Active Learning in 3D Biomedical Imagingcited by 1
  7. 2024Uncertainty-Error correlations in Evidential Deep Learning models for biomedical segmentationcited by 0