# Cost-Effective Active Learning for Melanoma Segmentation

arXiv: https://arxiv.org/abs/1711.09168 · HTML: https://mlsanity.com/paper/1711.09168/

## Key facts

- Cited by 41 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Nov 2017.

## Built on by (most cited first, 30 of 41)

- [A comprehensive survey on deep active learning in medical image analysis](https://mlsanity.com/paper/2310.14230/) (2023)
- [All you need are a few pixels: semantic segmentation with PixelPick](https://mlsanity.com/paper/2104.06394/) (2021)
- [DSAL: Deeply Supervised Active Learning from Strong and Weak Labelers for Biomedical Image Segmentation](https://mlsanity.com/paper/2101.09057/) (2021)
- [Interpretability-Driven Sample Selection Using Self Supervised Learning For Disease Classification And Segmentation](https://mlsanity.com/paper/2104.06087/) (2021)
- [Boosting Active Learning via Improving Test Performance](https://mlsanity.com/paper/2112.05683/) (2021)
- [LiDAL: Inter-frame Uncertainty Based Active Learning for 3D LiDAR Semantic Segmentation](https://mlsanity.com/paper/2211.05997/) (2022)
- [Deep Active Learning with Noise Stability](https://mlsanity.com/paper/2205.13340/) (2022)
- [Towards Free Data Selection with General-Purpose Models](https://mlsanity.com/paper/2309.17342/) (2023)
- [Patient Aware Active Learning for Fine-Grained OCT Classification](https://mlsanity.com/paper/2206.11485/) (2022)
- [Breaking the Barrier: Selective Uncertainty-based Active Learning for Medical Image Segmentation](https://mlsanity.com/paper/2401.16298/) (2024)
- [Self-supervised Assisted Active Learning for Skin Lesion Segmentation](https://mlsanity.com/paper/2205.07021/) (2022)
- [Less Is More: A Comparison of Active Learning Strategies for 3D Medical Image Segmentation](https://mlsanity.com/paper/2207.00845/) (2022)
- [Deep Active Learning for Computer Vision: Past and Future](https://mlsanity.com/paper/2211.14819/) (2022)
- [Reducing Annotating Load: Active Learning with Synthetic Images in Surgical Instrument Segmentation](https://mlsanity.com/paper/2108.03534/) (2021)
- [CPRAL: Collaborative Panoptic-Regional Active Learning for Semantic Segmentation](https://mlsanity.com/paper/2112.05975/) (2021)
- [Towards General and Efficient Active Learning](https://mlsanity.com/paper/2112.07963/) (2021)
- [A Review of Bayesian Uncertainty Quantification in Deep Probabilistic Image Segmentation](https://mlsanity.com/paper/2411.16370/) (2024)
- [Deep Active Learning in the Presence of Label Noise: A Survey](https://mlsanity.com/paper/2302.11075/) (2023)
- [USIM-DAL: Uncertainty-aware Statistical Image Modeling-based Dense Active Learning for Super-resolution](https://mlsanity.com/paper/2305.17520/) (2023)
- [Predictive Accuracy-Based Active Learning for Medical Image Segmentation](https://mlsanity.com/paper/2405.00452/) (2024)
- [MoBYv2AL: Self-supervised Active Learning for Image Classification](https://mlsanity.com/paper/2301.01531/) (2023)
- [EdgeAL: An Edge Estimation Based Active Learning Approach for OCT Segmentation](https://mlsanity.com/paper/2307.10745/) (2023)
- [Deep Bayesian Active Learning-to-Rank with Relative Annotation for Estimation of Ulcerative Colitis Severity](https://mlsanity.com/paper/2409.04952/) (2024)
- [Region-level Active Detector Learning](https://mlsanity.com/paper/2108.09186/) (2021)
- [An Active and Contrastive Learning Framework for Fine-Grained Off-Road Semantic Segmentation](https://mlsanity.com/paper/2202.09002/) (2022)
- [Knowing What to Label for Few Shot Microscopy Image Cell Segmentation](https://mlsanity.com/paper/2211.10244/) (2022)
- [Active Learning for Video Classification with Frame Level Queries](https://mlsanity.com/paper/2307.05587/) (2023)
- [Deep Active Learning with Manifold-preserving Trajectory Sampling](https://mlsanity.com/paper/2410.15605/) (2024)
- [GCI-ViTAL: Gradual Confidence Improvement with Vision Transformers for Active Learning on Label Noise](https://mlsanity.com/paper/2411.05939/) (2024)
- [Integrating Semi-Supervised and Active Learning for Semantic Segmentation](https://mlsanity.com/paper/2501.19227/) (2025)

## Abstract (arXiv)

We propose a novel Active Learning framework capable to train effectively a convolutional neural network for semantic segmentation of medical imaging, with a limited amount of training labeled data. Our contribution is a practical Cost-Effective Active Learning approach using dropout at test time as Monte Carlo sampling to model the pixel-wise uncertainty and to analyze the image information to improve the training performance. The source code of this project is available at https://marc-gorriz.github.io/CEAL-Medical-Image-Segmentation/ .