# Active Learning for Segmentation Based on Bayesian Sample Queries

arXiv: https://arxiv.org/abs/1912.10493 · HTML: https://mlsanity.com/paper/1912.10493/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Dec 2019.

## Built on by (most cited first, 12 of 12)

- [A comprehensive survey on deep active learning in medical image analysis](https://mlsanity.com/paper/2310.14230/) (2023)
- [Active learning for medical image segmentation with stochastic batches](https://mlsanity.com/paper/2301.07670/) (2023)
- [Label-Efficient Deep Learning in Medical Image Analysis: Challenges and Future Directions](https://mlsanity.com/paper/2303.12484/) (2023)
- [Discwise Active Learning for LiDAR Semantic Segmentation](https://mlsanity.com/paper/2309.13276/) (2023)
- [Data efficient deep learning for medical image analysis: A survey](https://mlsanity.com/paper/2310.06557/) (2023)
- [A Review of Bayesian Uncertainty Quantification in Deep Probabilistic Image Segmentation](https://mlsanity.com/paper/2411.16370/) (2024)
- [A Survey on Active Deep Learning: From Model-driven to Data-driven](https://mlsanity.com/paper/2101.09933/) (2021)
- [Hybrid Representation-Enhanced Sampling for Bayesian Active Learning in Musculoskeletal Segmentation of Lower Extremities](https://mlsanity.com/paper/2307.13986/) (2023)
- [Better than Average: Spatially-Aware Aggregation of Segmentation Uncertainty Improves Downstream Performance](https://mlsanity.com/paper/2603.29941/) (2026)
- [Ensemble uncertainty as a criterion for dataset expansion in distinct bone segmentation from upper-body CT images](https://mlsanity.com/paper/2208.09216/) (2022)
- [Is Complete Labeling Necessary? Understanding Active Learning in Longitudinal Medical Imaging](https://mlsanity.com/paper/2511.18007/) (2025)
- [ActiveFreq: Integrating Active Learning and Frequency Domain Analysis for Interactive Segmentation](https://mlsanity.com/paper/2603.11498/) (2026)

## Abstract (arXiv)

Segmentation of anatomical structures is a fundamental image analysis task for many applications in the medical field. Deep learning methods have been shown to perform well, but for this purpose large numbers of manual annotations are needed in the first place, which necessitate prohibitive levels of resources that are often unavailable. In an active learning framework of selecting informed samples for manual labeling, expert clinician time for manual annotation can be optimally utilized, enabling the establishment of large labeled datasets for machine learning. In this paper, we propose a novel method that combines representativeness with uncertainty in order to estimate ideal samples to be annotated, iteratively from a given dataset. Our novel representativeness metric is based on Bayesian sampling, by using information-maximizing autoencoders. We conduct experiments on a shoulder magnetic resonance imaging (MRI) dataset for the segmentation of four musculoskeletal tissue classes. Quantitative results show that the annotation of representative samples selected by our proposed querying method yields an improved segmentation performance at each active learning iteration, compared to a baseline method that also employs uncertainty and representativeness metrics. For instance, with only 10% of the dataset annotated, our method reaches within 5% of Dice score expected from the upper bound scenario of all the dataset given as annotated (an impractical scenario due to resource constraints), and this gap drops down to a mere 2% when less than a fifth of the dataset samples are annotated. Such active learning approach to selecting samples to annotate enables an optimal use of the expert clinician time, being often the bottleneck in realizing machine learning solutions in medicine.