

@article{WANG2024103201,
title = {A comprehensive survey on deep active learning in medical image analysis},
journal = {Medical Image Analysis},
volume = {95},
pages = {103201},
year = {2024},
issn = {1361-8415},
author = {Haoran Wang and Qiuye Jin and Shiman Li and Siyu Liu and Manning Wang and Zhijian Song},
keywords = {Active learning, Medical image analysis, Survey, Deep learning},
abstract = {Deep learning has achieved widespread success in medical image analysis, leading to an increasing demand for large-scale expert-annotated medical image datasets. Yet, the high cost of annotating medical images severely hampers the development of deep learning in this field. To reduce annotation costs, active learning aims to select the most informative samples for annotation and train high-performance models with as few labeled samples as possible. In this survey, we review the core methods of active learning, including the evaluation of informativeness and sampling strategy. For the first time, we provide a detailed summary of the integration of active learning with other label-efficient techniques, such as semi-supervised, self-supervised learning, and so on. We also summarize active learning works that are specifically tailored to medical image analysis. Additionally, we conduct a thorough comparative analysis of the performance of different AL methods in medical image analysis with experiments. In the end, we offer our perspectives on the future trends and challenges of active learning and its applications in medical image analysis. An accompanying paper list and code for the comparative analysis is available in https://github.com/LightersWang/Awesome-Active-Learning-for-Medical-Image-Analysis.}
}

@misc{radford_LearningTransferableVisual_2021,
  title = {Learning {{Transferable Visual Models From Natural Language Supervision}}},
  author = {Radford, Alec and Kim, Jong Wook and Hallacy, Chris and Ramesh, Aditya and Goh, Gabriel and Agarwal, Sandhini and Sastry, Girish and Askell, Amanda and Mishkin, Pamela and Clark, Jack and Krueger, Gretchen and Sutskever, Ilya},
  year = 2021,
  number = {arXiv:2103.00020},
  eprint = {2103.00020},
  primaryclass = {cs},
  publisher = {arXiv},
  abstract = {State-of-the-art computer vision systems are trained to predict a fixed set of predetermined object categories. This restricted form of supervision limits their generality and usability since additional labeled data is needed to specify any other visual concept. Learning directly from raw text about images is a promising alternative which leverages a much broader source of supervision. We demonstrate that the simple pre-training task of predicting which caption goes with which image is an efficient and scalable way to learn SOTA image representations from scratch on a dataset of 400 million (image, text) pairs collected from the internet. After pre-training, natural language is used to reference learned visual concepts (or describe new ones) enabling zero-shot transfer of the model to downstream tasks. We study the performance of this approach by benchmarking on over 30 different existing computer vision datasets, spanning tasks such as OCR, action recognition in videos, geo-localization, and many types of fine-grained object classification. The model transfers non-trivially to most tasks and is often competitive with a fully supervised baseline without the need for any dataset specific training. For instance, we match the accuracy of the original ResNet-50 on ImageNet zero-shot without needing to use any of the 1.28 million training examples it was trained on. We release our code and pre-trained model weights at https://github.com/OpenAI/CLIP.},
  archiveprefix = {arXiv},
  keywords = {VisionLanguageModels},
  file = {/Users/nini/Zotero/storage/SDJMEHPG/Radford et al_2021_Learning Transferable Visual Models From Natural Language Supervision.pdf;/Users/nini/Zotero/storage/72GBVQS4/2103.html}
}


@inproceedings{pang2024fairness,
  title        = {Fairness Without Harm: An Influence-Guided Active Sampling Approach},
  author       = {Pang, Jinlong and Wang, Jialu and Zhu, Zhaowei and Yao, Yuanshun and Qian, Chen and Liu, Yang},
  booktitle    = {Advances in Neural Information Processing Systems (NeurIPS)},
  year         = {2024}
}
@article{gaillochet2023stochastic,
  title   = {Active learning for medical image segmentation with stochastic batches},
  author  = {Gaillochet, M{\'e}lanie and Desrosiers, Christian and Lombaert, Herv{\'e}},
  journal = {Medical Image Analysis},
  volume  = {90},
  pages   = {102958},
  year    = {2023},
}
@article{ANAHIDEH2022116981,
title = {Fair active learning},
journal = {Expert Systems with Applications},
volume = {199},
pages = {116981},
year = {2022},
author = {Hadis Anahideh and Abolfazl Asudeh and Saravanan Thirumuruganathan},
keywords = {Active learning, Algorithmic fairness, Limited labeled data},
abstract = {Machine learning (ML) is increasingly being used in high-stakes applications impacting society. Therefore, it is of critical importance that ML models do not propagate discrimination. Collecting accurate labeled data in societal applications is challenging and costly. Active learning is a promising approach to build an accurate classifier by interactively querying an oracle within a labeling budget. We introduce the fair active learning framework to carefully select data points to be labeled so as to balance model accuracy and fairness. To incorporate the notion of fairness in the active learning sampling core, it is required to measure the fairness of the model after adding each unlabeled sample. Since their labels are unknown in advance, we propose an expected fairness metric to probabilistically measure the impact of each sample if added for each possible class label. Next, we propose multiple optimizations to balance the trade-off between accuracy and fairness. Our first optimization linearly aggregate the expected fairness with entropy using a control parameter. To avoid erroneous estimation of the expected fairness, we propose a nested approach to maintain the accuracy of the model, limiting the search space to the top bucket of sample points with large entropy. Finally, to ensure the unfairness reduction of the model after labeling, we propose to replicate the points that truly reduce the unfairness after labeling. We demonstrate the effectiveness and efficiency of our proposed algorithms over widely used benchmark datasets using demographic parity and equalized odds notions of fairness.}
}

@article{YANG2023175,
title = {Interactive active learning for fairness with partial group label},
journal = {AI Open},
volume = {4},
pages = {175-182},
year = {2023},
issn = {2666-6510},
author = {Zeyu Yang and Jizhi Zhang and Fuli Feng and Chongming Gao and Qifan Wang and Xiangnan He},
keywords = {AI ethics, Trust, Fairness, Humans and AI, Active learning},
abstract = {The rapid development of AI technologies has found numerous applications across various domains in human society. Ensuring fairness and preventing discrimination are critical considerations in the development of AI models. However, incomplete information often hinders the complete collection of sensitive attributes in real-world applications, primarily due to the high cost and potential privacy violations associated with such data collection. Label reconstruction through building another learner on sensitive attributes is a common approach to address this issue. However, existing methods focus solely on improving the prediction accuracy of the sensitive learner as a separate model, while ignoring the disparity between its accuracy and the fairness of the base model. To bridge this gap, this paper proposes an interactive learning framework that aims to optimize the sensitive learner while considering the fairness of the base learner. Furthermore, a new active sampling strategy is developed to select the most valuable data for the sensitive learner regarding the fairness of the base model. The effectiveness of our proposed method in improving model fairness is demonstrated through comprehensive evaluations conducted on various datasets and fairness criteria.}
}


@article{BUDD2021102062,
title = {A survey on active learning and human-in-the-loop deep learning for medical image analysis},
journal = {Medical Image Analysis},
volume = {71},
pages = {102062},
year = {2021},
issn = {1361-8415},
author = {Samuel Budd and Emma C. Robinson and Bernhard Kainz},
keywords = {Medical image analysis, Deep Learning, Human-in-the-Loop, Active learning},
abstract = {Fully automatic deep learning has become the state-of-the-art technique for many tasks including image acquisition, analysis and interpretation, and for the extraction of clinically useful information for computer-aided detection, diagnosis, treatment planning, intervention and therapy. However, the unique challenges posed by medical image analysis suggest that retaining a human end-user in any deep learning enabled system will be beneficial. In this review we investigate the role that humans might play in the development and deployment of deep learning enabled diagnostic applications and focus on techniques that will retain a significant input from a human end user. Human-in-the-Loop computing is an area that we see as increasingly important in future research due to the safety-critical nature of working in the medical domain. We evaluate four key areas that we consider vital for deep learning in the clinical practice: (1) Active Learning to choose the best data to annotate for optimal model performance; (2) Interaction with model outputs - using iterative feedback to steer models to optima for a given prediction and offering meaningful ways to interpret and respond to predictions; (3) Practical considerations - developing full scale applications and the key considerations that need to be made before deployment; (4) Future Prospective and Unanswered Questions - knowledge gaps and related research fields that will benefit human-in-the-loop computing as they evolve. We offer our opinions on the most promising directions of research and how various aspects of each area might be unified towards common goals.}
}


@article{Wang2022MitigatingBiasLimitedAnnotations,
  author        = {Wang, Guanchu and Du, Mengnan and Liu, Ninghao and Zou, Na and Hu, Xia},
  title         = {Mitigating Algorithmic Bias with Limited Annotations},
  journal       = {arXiv preprint arXiv:2207.10018},
  year          = {2022},
  archivePrefix = {arXiv},
  eprint        = {2207.10018},
  primaryClass  = {cs.LG}
}

@article{Fajri2024FALCUR,
  author  = {Fajri, Ricky Maulana and Saxena, Akrati and Pei, Yulong and Pechenizkiy, Mykola},
  title   = {FAL-CUR: Fair Active Learning using Uncertainty and Representativeness on Fair Clustering},
  journal = {Expert Systems with Applications},
  volume  = {242},
  pages   = {122842},
  year    = {2024},
}


@article{Danaee2025InvestigatingDemographicBias,
  author  = {Danaee, Ghazal and Niethammer, Marc and Rushmore, Jarrett and Bouix, Sylvain},
  title   = {Investigating Demographic Bias in Brain {MRI} Segmentation: A Comparative Study of Deep-Learning and Non-Deep-Learning Methods},
  journal = {arXiv preprint arXiv:2510.17999},
  year    = {2025},
  archivePrefix = {arXiv},
  eprint  = {2510.17999},
  primaryClass = {cs.CV},
}


@inproceedings{Ioannou2022DemographicBiasBrainMR,
  author    = {Stefanos Ioannou and
               Hana Chockler and
               Alexander Hammers and
               Andrew P. King},
  title     = {A Study of Demographic Bias in {CNN}-Based Brain {MR} Segmentation},
  booktitle = {Machine Learning in Clinical Neuroimaging (MLCN 2022)},
  series    = {Lecture Notes in Computer Science},
  volume    = {13596},
  pages     = {13--22},
  publisher = {Springer},
  year      = {2022},
}

@inproceedings{Stanley2023SimBA,
  author    = {Stanley, Emma A. M. and Wilms, Matthias and Forkert, Nils D.},
  title     = {A Flexible Framework for Simulating and Evaluating Biases in Deep Learning-Based Medical Image Analysis},
  booktitle = {Medical Image Computing and Computer-Assisted Intervention -- MICCAI 2023},
  year      = {2023},
  publisher = {Springer},
  series    = {Lecture Notes in Computer Science},
  volume    = {14221},
  pages     = {488--498},
}


@inproceedings{Tian2024FairSeg,
  author    = {Tian, Yu and Shi, Min and Luo, Yan and Kouhana, Ava and Elze, Tobias and Wang, Mengyu},
  title     = {FairSeg: A Large-Scale Medical Image Segmentation Dataset for Fairness Learning Using Segment Anything Model with Fair Error-Bound Scaling},
  booktitle = {Proceedings of the Twelfth International Conference on Learning Representations (ICLR)},
  year      = {2024},
  archivePrefix = {arXiv},
  eprint    = {2311.02189},
}


@article{Kim2024Active,
  title   = {Active Learning in Brain Tumor Segmentation with Uncertainty Sampling and Annotation Redundancy Restriction},
  author  = {Kim, Daniel D. and Chandra, Rajat S. and Yang, Li and Wu, Jing and Feng, Xue and Atalay, Michael and Bettegowda, Chetan and Jones, Craig and Sair, Haris and Liao, Wei Hua and Zhu, Chengzhang and Zou, Beiji and Kazerooni, Anahita Fathi and Nabavizadeh, Ali and Jiao, Zhicheng and Peng, Jian and Bai, Harrison X.},
  journal = {Journal of Imaging Informatics in Medicine},
  year    = {2024},
  volume  = {37},
  number  = {5},
  pages   = {2099--2107},
  publisher = {Springer Nature}
}
@article{Atzeni2022Deep,
  title   = {Deep active learning for suggestive segmentation of biomedical image stacks via optimisation of Dice scores and traced boundary length},
  author  = {Atzeni, Alessia and Peter, Loic and Robinson, Eleanor and Blackburn, Emily and Althonayan, Juri and Alexander, Daniel C. and Iglesias, Juan Eugenio},
  journal = {Medical Image Analysis},
  volume  = {81},
  pages   = {102549},
  year    = {2022},
}
@article{Boehringer2023Active,
  title   = {An active learning approach to train a deep learning algorithm for tumor segmentation from brain MR images},
  author  = {Boehringer, Andrew S. and Sanaat, Amirhossein and Arabi, Hossein and Zaidi, Habib},
  journal = {Insights into Imaging},
  year    = {2023},
  volume  = {14},
  number  = {1},
  pages   = {141},
  publisher = {Springer},
}
@article{Qu2024Rethinking,
  title   = {Rethinking deep active learning for medical image segmentation: A diffusion and angle-based framework},
  author  = {Qu, Linhao and Jin, Qiuye and Fu, Kexue and Wang, Manning and Song, Zhijian},
  journal = {Biomedical Signal Processing and Control},
  volume  = {96},
  pages   = {106493},
  year    = {2024},
  publisher = {Elsevier}
}
@inproceedings{Wang2025VLMFairADA,
  title     = {Toward Fair and Accurate Cross-Domain Medical Image Segmentation: A VLM-Driven Active Domain Adaptation Paradigm},
  author    = {Wang, Hongqiu and Chen, Wu and Luo, Xiangde and Xing, Zhaohu and Liu, Lihao and Qin, Jing and Wu, Shaozhi and Zhu, Lei},
  booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
  year      = {2025},
  pages     = {24102--24112}
}


@inproceedings{Weerts2023ActiveSelectionBias,
  author    = {Weerts, Hilde and {van der Waa}, Jasper and {van den Bosch}, Antske and {van der Aalst}, Wil M. P.},
  title     = {Active Learning Mitigates Selection Bias and Group Disparities},
  booktitle = {Proceedings of the AAAI Workshop on Algorithmic Fairness through the Lens of Causality and Interpretability (AFUCI)},
  year      = {2023},
  series    = {CEUR Workshop Proceedings},
  volume    = {3470},
  pages     = {74--88},
}


@article{Frazier2008,
  author    = {Jean A. Frazier and Steven M. Hodge and Janis L. Breeze and Anthony J. Giuliano and Janine E. Terry and Constance M. Moore and David N. Kennedy and Melissa P. Lopez-Larson and Verne S. Caviness and Larry J. Seidman and Benjamin Zablotsky and Nikos Makris},
  title     = {Diagnostic and sex effects on limbic volumes in early-onset bipolar disorder and schizophrenia},
  journal   = {Schizophrenia Bulletin},
  volume    = {34},
  number    = {1},
  pages     = {37--46},
  year      = {2008},
  pmid      = {18003631},
  pmcid     = {2632388},
}


@article{Isamah2010,
  author    = {Isamah, Nneka and Faison, Warachal and Payne, Martha E. and MacFall, James and Steffens, David C. and Beyer, John L. and Krishnan, K. Ranga and Taylor, Warren D.},
  title     = {Variability in frontotemporal brain structure: the importance of recruitment of African Americans in neuroscience research},
  journal   = {PLoS One},
  volume    = {5},
  number    = {10},
  pages     = {e13642},
  year      = {2010},
}

@article{Stanley2022,
  author    = {Emma A. M. Stanley and Matthias Wilms and Pauline Mouches and Nils D. Forkert},
  title     = {Fairness-related performance and explainability effects in deep learning models for brain image analysis},
  journal   = {J. Med. Imaging (Bellingham)},
  volume    = {9},
  number    = {6},
  pages     = {061102},
  year      = {2022},
}

@article{Dibaji2024,
  author = {Dibaji, Mahsa and Ospel, Jana and Souza, Roberto and Bento, Mariana},
  title = {Sex differences in brain {MRI} using deep learning toward fairer healthcare outcomes},
  journal = {Frontiers in Computational Neuroscience},
  volume = {18},
  number = {1452457},

  year = {2024},

}
@misc{mehrabi2022surveybiasfairnessmachine,
      title={A Survey on Bias and Fairness in Machine Learning}, 
      author={Ninareh Mehrabi and Fred Morstatter and Nripsuta Saxena and Kristina Lerman and Aram Galstyan},
      year={2022},
      eprint={1908.09635},
      archivePrefix={arXiv},
      primaryClass={cs.LG},
}

@article{siddiqui2024fair,
  title       = {Fair AI-powered orthopedic image segmentation: addressing bias and promoting equitable healthcare},
  author    = {Siddiqui, Ismaeel A. and Littlefield, Nickolas and Carlson, Luke A. and
               Chhabra, Avani and Menezes, Zoe and Thakar, Sakshi Mehul and
               Abedian, Mehrnaz and Mastorakos, George M. and Moradi, Hamidreza and
               Amirian, Soheyla and Gong, Matthew and Lohse, Ines and Weiss, Kurt R. and
               Plate, Johannes F. and Tafti, Ahmad P.},
  journal     = {Scientific Reports},
  year        = {2024},
}


@incollection{puyolanton2021fairnesscardiacmrimage,
  author    = {Puyol-Ant\'on, Esther and Ruijsink, Bram and Piechnik, Stefan K. and
               Neubauer, Stefan and Petersen, Steffen E. and Razavi, Reza and King, Andrew P.},
  title     = {Fairness in Cardiac MR Image Analysis: An Investigation of Bias Due to Data Imbalance in Deep Learning Based Segmentation},
  booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2021},
  editor    = {de Bruijne, Marleen and Cattin, Philippe C. and Cotin, St{\'e}phane and
               Padoy, Nicolas and Speidel, Stefanie and Zheng, Yefeng and Essert, Caroline},
  series    = {Lecture Notes in Computer Science},
  volume    = {12903},
  pages     = {413--423},
  publisher = {Springer, Cham},
  year      = {2021},
}
@ARTICLE{Puyol-Antón2022,
  AUTHOR={Puyol-Ant\'on, Esther and Ruijsink, Bram and Piechnik, Stefan K. and Neubauer, Stefan and Petersen, Steffen E. and Razavi, Reza and King, Andrew P.},
  TITLE={Fairness in Cardiac Magnetic Resonance Imaging: Assessing Sex and Racial Bias in Deep Learning-Based Segmentation},
  JOURNAL={Frontiers in Cardiovascular Medicine},
  VOLUME={9},
  YEAR={2022},
}


@article{lee2025investigation,
 author = {Lee, Tiarna and Puyol-Antón, Esther and Ruijsink, Bram and Roujol, Sebastien and Barfoot, Theodore and Ogbomo-Harmitt, Shaheim and Shi, Miaojing and King, Andrew},
    title = {An investigation into the causes of race bias in artificial intelligence–based cine cardiac magnetic resonance segmentation},
    journal = {European Heart Journal - Digital Health},
    pages = {ztaf008},
    year = {2025},
    abstract = {Artificial intelligence (AI) methods are being used increasingly for the automated segmentation of cine cardiac magnetic resonance (CMR) imaging. However, these methods have been shown to be subject to race bias; i.e. they exhibit different levels of performance for different races depending on the (im)balance of the data used to train the AI model. In this paper, we investigate the source of this bias, seeking to understand its root cause(s).We trained AI models to perform race classification on cine CMR images and/or segmentations from White and Black subjects from the UK Biobank and found that the classification accuracy for images was higher than for segmentations. Interpretability methods showed that the models were primarily looking at non-heart regions. Cropping images tightly around the heart caused classification accuracy to drop to almost chance level. Visualizing the latent space of AI segmentation models showed that race information was encoded in the models. Training segmentation models using cropped images reduced but did not eliminate the bias. A number of possible confounders for the bias in segmentation model performance were identified for Black subjects but none for White subjects.Distributional differences between annotated CMR data of White and Black races, which can lead to bias in trained AI segmentation models, are predominantly image-based, not segmentation-based. Most of the differences occur in areas outside the heart, such as subcutaneous fat. These findings will be important for researchers investigating performance of AI models on different races.},
    issn = {2634-3916},
}

@unpublished{alqarni2024investigation,
  author       = {Alqarni, Maram and Jones, Emma and Ribeiro, Luis and Hema, Verma and
                  Cooper, Sian and Mullassery, Vinod and Morris, Stephen and
                  Guerrero-Urbano, Teresa and King, Andrew},
  title        = {An Investigation of Race Bias in Deep Learning-Based Segmentation of Prostate MRI Images},
  note         = {SSRN Preprint},
  year         = {2024},
}

@article{bencevic2024SkinColorBias,
  author = {Marin Ben{\v{c}}evi{\'c} and Marija Habijan and Irena Gali{\'c} and Danilo Babin and Aleksandra Pi{\v{z}}urica},
  title = {Understanding skin color bias in deep learning--based skin lesion segmentation},
  journal = {Computer Methods and Programs in Biomedicine},
  volume = {245},
  pages = {108044},
  year = {2024},
}


@inproceedings{camilleri2024fair,
  title     = {Fair Active Learning in Low-Data Regimes},
  author    = {Camilleri, Romain and Wagenmaker, Andrew and Morgenstern, Jamie and Jain, Lalit and Jamieson, Kevin},
  booktitle = {Proceedings of the Fortieth Conference on Uncertainty in Artificial Intelligence},
  series    = {Proceedings of Machine Learning Research},
  volume    = {244},
  pages     = {517--531},
  year      = {2024},
  editor    = {Kiyavash, Negar and Mooij, Joris M.},
  publisher = {PMLR},
}


@InProceedings{Munjal_2022_CVPR,
  author    = {Munjal, Prateek and Hayat, Nasir and Hayat, Munawar and Sourati, Jamshid and Khan, Shadab},
  title     = {Towards Robust and Reproducible Active Learning Using Neural Networks},
  booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year      = {2022},
  pages     = {223--232}
}
