ProtoAL: Interpretable Deep Active Learning with prototypes for medical imaging
Not on the map. Why?
Abstract (arXiv)
The adoption of Deep Learning algorithms in the medical imaging field is a prominent area of research, with high potential for advancing AI-based Computer-aided diagnosis (AI-CAD) solutions. However, current solutions face challenges due to a lack of interpretability features and high data demands, prompting recent efforts to address these issues. In this study, we propose the ProtoAL method, where we integrate an interpretable DL model into the Deep Active Learning (DAL) framework. This approach aims to address both challenges by focusing on the medical imaging context and utilizing an inherently interpretable model based on prototypes. We evaluated ProtoAL on the Messidor dataset, achieving an area under the precision-recall curve of 0.79 while utilizing only 76.54% of the available labeled data. These capabilities can enhances the practical usability of a DL model in the medical field, providing a means of trust calibration in domain experts and a suitable solution for learning in the data scarcity context often found.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 19 of its 32 references that are in the map. First posted on arXiv 6 Apr 2024.
Builds on
19 of 32 references.
References · 19
- 2015Deep Residual Learning for Image Recognitioncited by 51,809
- 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,267
- 2014ImageNet Large Scale Visual Recognition Challengecited by 8,441
- 2016Densely Connected Convolutional Networkscited by 6,691
- 2015Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learningcited by 4,416
- 2017A Survey on Deep Learning in Medical Image Analysiscited by 1,182
- 2018This Looks Like That: Deep Learning for Interpretable Image Recognitioncited by 731
- 2020Image Segmentation Using Deep Learning: A Surveycited by 659
- 2019A Survey of Deep Learning-based Object Detectioncited by 106
- 2021XProtoNet: Diagnosis in Chest Radiography with Global and Local Explanationscited by 72
- 2021DSAL: Deeply Supervised Active Learning from Strong and Weak Labelers for Biomedical Image Segmentationcited by 27
- 2022Warm Start Active Learning with Proxy Labels & Selection via Semi-Supervised Fine-Tuningcited by 21
7 more
- 2020Deep Active Learning for Joint Classification & Segmentation with Weak Annotatorcited by 16
- 2023MProtoNet: A Case-Based Interpretable Model for Brain Tumor Classification with 3D Multi-parametric Magnetic Resonance Imagingcited by 11
- 2023ProtoASNet: Dynamic Prototypes for Inherently Interpretable and Uncertainty-Aware Aortic Stenosis Classification in Echocardiographycited by 11
- 2019Effectiveness of Tree-based Ensembles for Anomaly Discovery: Insights, Batch and Streaming Active Learningcited by 9
- 2019O-MedAL: Online Active Deep Learning for Medical Image Analysiscited by 6
- 2019Deep Active Learning for Axon-Myelin Segmentation on Histology Datacited by 3
- 2020Deep Active Learning by Model Interpretabilitycited by 2
Built on by
Citing papers per year: 2024: 1; 2025: 1.
2 papers.