# Active learning using adaptable task-based prioritisation

arXiv: https://arxiv.org/abs/2212.01703 · HTML: https://mlsanity.com/paper/2212.01703/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 43 references that are in the map.
- First posted on arXiv 3 Dec 2022.

## Builds on (most cited first, 25 of 25)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning](https://mlsanity.com/paper/1506.02142/) (2015)
- [3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation](https://mlsanity.com/paper/1606.06650/) (2016)
- [On First-Order Meta-Learning Algorithms](https://mlsanity.com/paper/1803.02999/) (2018)
- [The Medical Segmentation Decathlon](https://mlsanity.com/paper/2106.05735/) (2021)
- [The Liver Tumor Segmentation Benchmark (LiTS)](https://mlsanity.com/paper/1901.04056/) (2019)
- [RL2: Fast Reinforcement Learning via Slow Reinforcement Learning](https://mlsanity.com/paper/1611.02779/) (2016)
- [Learning to reinforcement learn](https://mlsanity.com/paper/1611.05763/) (2016)
- [A Probabilistic U-Net for Segmentation of Ambiguous Images](https://mlsanity.com/paper/1806.05034/) (2018)
- [A Survey on Active Learning and Human-in-the-Loop Deep Learning for Medical Image Analysis](https://mlsanity.com/paper/1910.02923/) (2019)
- [Suggestive Annotation: A Deep Active Learning Framework for Biomedical Image Segmentation](https://mlsanity.com/paper/1706.04737/) (2017)
- [Data Valuation using Reinforcement Learning](https://mlsanity.com/paper/1909.11671/) (2019)
- [Learning how to Active Learn: A Deep Reinforcement Learning Approach](https://mlsanity.com/paper/1708.02383/) (2017)
- [Is segmentation uncertainty useful?](https://mlsanity.com/paper/2103.16265/) (2021)
- [Semi-supervised few-shot learning for medical image segmentation](https://mlsanity.com/paper/2003.08462/) (2020)
- [Active One-shot Learning](https://mlsanity.com/paper/1702.06559/) (2017)
- [MedAL: Deep Active Learning Sampling Method for Medical Image Analysis](https://mlsanity.com/paper/1809.09287/) (2018)
- [Active Learning for Segmentation by Optimizing Content Information for Maximal Entropy](https://mlsanity.com/paper/1807.06962/) (2018)
- [Image quality assessment for machine learning tasks using meta-reinforcement learning](https://mlsanity.com/paper/2203.14258/) (2022)
- [Image quality assessment by overlapping task-specific and task-agnostic measures: application to prostate multiparametric MR images for cancer segmentation](https://mlsanity.com/paper/2202.09798/) (2022)
- [Adaptable image quality assessment using meta-reinforcement learning of task amenability](https://mlsanity.com/paper/2108.04359/) (2021)
- [Few-shot image segmentation for cross-institution male pelvic organs using registration-assisted prototypical learning](https://mlsanity.com/paper/2201.06358/) (2022)
- [Learning image quality assessment by reinforcing task amenable data selection](https://mlsanity.com/paper/2102.07615/) (2021)
- [Learning to reinforcement learn for Neural Architecture Search](https://mlsanity.com/paper/1911.03769/) (2019)

## Built on by (most cited first, 6 of 6)

- [Arctique: An artificial histopathological dataset unifying realism and controllability for uncertainty quantification](https://mlsanity.com/paper/2411.07097/) (2024)
- [SPARS: Self-Play Adversarial Reinforcement Learning for Segmentation of Liver Tumours](https://mlsanity.com/paper/2505.18989/) (2025)
- [Progressive Growing of Patch Size: Curriculum Learning for Accelerated and Improved Medical Image Segmentation](https://mlsanity.com/paper/2510.23241/) (2025)
- [Multi-task Learning with Active Learning for Arabic Offensive Speech Detection](https://mlsanity.com/paper/2506.02753/) (2025)
- [Active few-shot segmentation by reinforcing data selection](https://mlsanity.com/paper/2607.22371/) (2026)
- [Bayesian adaptively-weighted ensembles for few-shot abdominal segmentation](https://mlsanity.com/paper/2608.05815/) (2026)

## Abstract (arXiv)

Supervised machine learning-based medical image computing applications necessitate expert label curation, while unlabelled image data might be relatively abundant. Active learning methods aim to prioritise a subset of available image data for expert annotation, for label-efficient model training. We develop a controller neural network that measures priority of images in a sequence of batches, as in batch-mode active learning, for multi-class segmentation tasks. The controller is optimised by rewarding positive task-specific performance gain, within a Markov decision process (MDP) environment that also optimises the task predictor. In this work, the task predictor is a segmentation network. A meta-reinforcement learning algorithm is proposed with multiple MDPs, such that the pre-trained controller can be adapted to a new MDP that contains data from different institutes and/or requires segmentation of different organs or structures within the abdomen. We present experimental results using multiple CT datasets from more than one thousand patients, with segmentation tasks of nine different abdominal organs, to demonstrate the efficacy of the learnt prioritisation controller function and its cross-institute and cross-organ adaptability. We show that the proposed adaptable prioritisation metric yields converging segmentation accuracy for the novel class of kidney, unseen in training, using between approximately 40% to 60% of labels otherwise required with other heuristic or random prioritisation metrics. For clinical datasets of limited size, the proposed adaptable prioritisation offers a performance improvement of 22.6% and 10.2% in Dice score, for tasks of kidney and liver vessel segmentation, respectively, compared to random prioritisation and alternative active sampling strategies.