# MedAL: Deep Active Learning Sampling Method for Medical Image Analysis

arXiv: https://arxiv.org/abs/1809.09287 · HTML: https://mlsanity.com/paper/1809.09287/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Sep 2018.

## Built on by (most cited first, 20 of 20)

- [Active learning for medical image segmentation with stochastic batches](https://mlsanity.com/paper/2301.07670/) (2023)
- [Active Learning for Improved Semi-Supervised Semantic Segmentation in Satellite Images](https://mlsanity.com/paper/2110.07782/) (2021)
- [Less Is More: A Comparison of Active Learning Strategies for 3D Medical Image Segmentation](https://mlsanity.com/paper/2207.00845/) (2022)
- [Active Label Correction for Semantic Segmentation with Foundation Models](https://mlsanity.com/paper/2403.10820/) (2024)
- [Active learning using adaptable task-based prioritisation](https://mlsanity.com/paper/2212.01703/) (2022)
- [Hybrid Representation-Enhanced Sampling for Bayesian Active Learning in Musculoskeletal Segmentation of Lower Extremities](https://mlsanity.com/paper/2307.13986/) (2023)
- [Deep reinforced active learning for multi-class image classification](https://mlsanity.com/paper/2206.13391/) (2022)
- [Interactive Image Selection and Training for Brain Tumor Segmentation Network](https://mlsanity.com/paper/2406.03225/) (2024)
- [Active Learning to Classify Macromolecular Structures in situ for Less Supervision in Cryo-Electron Tomography](https://mlsanity.com/paper/2102.12040/) (2021)
- [Active Learning Based Domain Adaptation for Tissue Segmentation of Histopathological Images](https://mlsanity.com/paper/2303.05225/) (2023)
- [Robust Segmentation Models using an Uncertainty Slice Sampling Based Annotation Workflow](https://mlsanity.com/paper/2109.14879/) (2021)
- [Robust Framework for COVID-19 Identification from a Multicenter Dataset of Chest CT Scans](https://mlsanity.com/paper/2109.09241/) (2021)
- [PCDAL: A Perturbation Consistency-Driven Active Learning Approach for Medical Image Segmentation and Classification](https://mlsanity.com/paper/2306.16918/) (2023)
- [Dependency-aware Maximum Likelihood Estimation for Active Learning](https://mlsanity.com/paper/2503.05969/) (2025)
- [QuickDraw: Fast Visualization, Analysis and Active Learning for Medical Image Segmentation](https://mlsanity.com/paper/2503.09885/) (2025)
- [Deep Learning Approaches for Medical Imaging Under Varying Degrees of Label Availability: A Comprehensive Survey](https://mlsanity.com/paper/2504.11588/) (2025)
- [Parsimonious Dataset Construction for Laparoscopic Cholecystectomy Structure Segmentation](https://mlsanity.com/paper/2504.12573/) (2025)
- [Unifying Active Learning and Semi-Supervised Learning for Medical Image Segmentation](https://mlsanity.com/paper/2607.25014/) (2026)
- [How Many Labels Are Enough? ALDA: Active Learning Deployment Advisor for Medical Image Classification](https://mlsanity.com/paper/2608.03511/) (2026)
- [SPARCL: Spatially Adaptive Regional Coverage for Active Learning in Chest X-ray Analysis](https://mlsanity.com/paper/2610.17808/) (2026)

## Abstract (arXiv)

Deep learning models have been successfully used in medical image analysis problems but they require a large amount of labeled images to obtain good performance.Deep learning models have been successfully used in medical image analysis problems but they require a large amount of labeled images to obtain good performance. However, such large labeled datasets are costly to acquire. Active learning techniques can be used to minimize the number of required training labels while maximizing the model's performance.In this work, we propose a novel sampling method that queries the unlabeled examples that maximize the average distance to all training set examples in a learned feature space. We then extend our sampling method to define a better initial training set, without the need for a trained model, by using ORB feature descriptors. We validate MedAL on 3 medical image datasets and show that our method is robust to different dataset properties. MedAL is also efficient, achieving 80% accuracy on the task of Diabetic Retinopathy detection using only 425 labeled images, corresponding to a 32% reduction in the number of required labeled examples compared to the standard uncertainty sampling technique, and a 40% reduction compared to random sampling.