# Confident Coreset for Active Learning in Medical Image Analysis

arXiv: https://arxiv.org/abs/2004.02200 · HTML: https://mlsanity.com/paper/2004.02200/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Apr 2020.

## Built on by (most cited first, 5 of 5)

- [Model Performance-Guided Evaluation Data Selection for Effective Prompt Optimization](https://mlsanity.com/paper/2505.10736/) (2025)
- [Active Learning in Brain Tumor Segmentation with Uncertainty Sampling, Annotation Redundancy Restriction, and Data Initialization](https://mlsanity.com/paper/2302.10185/) (2023)
- [MoBYv2AL: Self-supervised Active Learning for Image Classification](https://mlsanity.com/paper/2301.01531/) (2023)
- [Active Learning Enhances Classification of Histopathology Whole Slide Images with Attention-based Multiple Instance Learning](https://mlsanity.com/paper/2303.01342/) (2023)
- [Information Gain Sampling for Active Learning in Medical Image Classification](https://mlsanity.com/paper/2208.00974/) (2022)

## Abstract (arXiv)

Recent advances in deep learning have resulted in great successes in various applications. Although semi-supervised or unsupervised learning methods have been widely investigated, the performance of deep neural networks highly depends on the annotated data. The problem is that the budget for annotation is usually limited due to the annotation time and expensive annotation cost in medical data. Active learning is one of the solutions to this problem where an active learner is designed to indicate which samples need to be annotated to effectively train a target model. In this paper, we propose a novel active learning method, confident coreset, which considers both uncertainty and distribution for effectively selecting informative samples. By comparative experiments on two medical image analysis tasks, we show that our method outperforms other active learning methods.