🎉🎉🎉 Our survey is accepted by Medical Image Analysis (IF = 10.9) !

In this repo, we provide a paper list of active learning in the fields of medical image analysis and computer vision. We focus on active learning papers of medical image analysis which were published on top-tier journals or conferences.

Also, we provide the code to evaluate different active learning methods on multiple medical imaging datasets of classification or segmentation. Please refer to the code folder.

This repo is mainly based on the following survey:
A comprehensive survey on deep active learning in medical image analysis
Haoran Wang, Qiuye Jin, Shiman Li, Siyu Liu, Manning Wang*, Zhijian Song*
* -- Corresponding Author
Paper | arXiv

If you find this repo or this survey paper helpful, please consider citing:

@article{wang2024comprehensive,
  title={A comprehensive survey on deep active learning in medical image analysis},
  author={Wang, Haoran and Jin, Qiuye and Li, Shiman and Liu, Siyu and Wang, Manning and Song, Zhijian},
  journal={Medical Image Analysis},
  pages={103201},
  year={2024},
  publisher={Elsevier}
}

Currently, I am actively updating this repo. Newly published papers and missing papers of related topics will be added. Please star us if you find this repo helpful ⭐

If you have any questions or suggestions, or if you think your paper fits our framework and should be added in the survey, feel free to contact me through e-mail ([email protected]) or create an issue in this repo.

We also welcome contributions to this repo for updating the paper list. Please submit a pull request if you want to contribute!

[PDF] - PDF link
[Code] - official code link
🕝 - not cited in our survey paper

A comprehensive survey on deep active learning in medical image analysis
[2024 MedIA] [PDF]

Label-efficient deep learning in medical image analysis: Challenges and future directions
[2023 arXiv] [PDF]

Deep Active Learning for Computer Vision: Past and Future
[2023 APSIPA Transactions on Signal and Information Processing] [PDF]

A comparative survey of deep active learning
[2022 arxiv] [PDF]

A survey on active deep learning: from model driven to data driven
[2022 ACM Computing Surveys] [PDF]

A survey on active learning and human-in-the-loop deep learning for medical image analysis
[2021 MedIA] [PDF]

A survey of deep active learning
[2021 ACM Computing Surveys] [PDF]

Embracing imperfect datasets: A review of deep learning solutions for medical image segmentation
[2020 MedIA] [PDF]

Active learning literature survey
[2009] [PDF]

ActiveDC: Distribution Calibration for Active Finetuning
🕝 [CVPR'24] [PDF]

Active Generalized Category Discovery
🕝 [CVPR'24] [PDF]

Revisiting the Domain Shift and Sample Uncertainty in Multi-source Active Domain Transfer
🕝 [CVPR'24] [PDF]

Active Prompt Learning in Vision Language Models
🕝 [CVPR'24] [PDF]

Plug and Play Active Learning for Object Detection
🕝 [CVPR'24] [PDF]

Think Twice Before Selection: Federated Evidential Active Learning for Medical Image Analysis with Domain Shifts
[CVPR'24] [PDF]

Re-Thinking Federated Active Learning Based on Inter-Class Diversity
🕝 [CVPR'23] [PDF] [Code]

Hybrid Active Learning via Deep Clustering for Video Action Detection
🕝 [CVPR'23] [PDF] [Code]

Are Binary Annotations Sufficient? Video Moment Retrieval via Hierarchical Uncertainty-Based Active Learning
🕝 [CVPR'23] [PDF] [Code]

MHPL: Minimum Happy Points Learning for Active Source Free Domain Adaptation
🕝 [CVPR'23] [PDF]

Active Finetuning: Exploiting Annotation Budget in the Pretraining-Finetuning Paradigm
[CVPR'23] [PDF] [Code]

Bi3D: Bi-Domain Active Learning for Cross-Domain 3D Object Detection
[CVPR'23] [PDF] [Code]

Divide and Adapt: Active Domain Adaptation via Customized Learning
[CVPR'23] [PDF] [Code]

Box-Level Active Detection
[CVPR'23] [PDF] [Code]

Entropy-Based Active Learning for Object Detection With Progressive Diversity Constraint
[CVPR'22] [PDF]

Active Learning for Open-Set Annotation
[CVPR'22] [PDF] [Code]

Meta Agent Teaming Active Learning for Pose Estimation
[CVPR'22] [PDF]

Towards Robust and Reproducible Active Learning Using Neural Networks
[CVPR'22] [PDF] [Code]

Active Learning by Feature Mixing
[CVPR'22] [PDF] [Code]

Which Images To Label for Few-Shot Medical Landmark Detection?
[CVPR'22] [PDF]

Towards Fewer Annotations: Active Learning via Region Impurity and Prediction Uncertainty for Domain Adaptive Semantic Segmentation
[CVPR'22] [PDF] [Code]

Learning Distinctive Margin Toward Active Domain Adaptation
[CVPR'22] [PDF] [Code]

BoostMIS: Boosting Medical Image Semi-Supervised Learning With Adaptive Pseudo Labeling and Informative Active Annotation
[CVPR'22] [PDF] [Code]

One-Bit Active Query With Contrastive Pairs
[CVPR'22] [PDF]

Revisiting Superpixels for Active Learning in Semantic Segmentation With Realistic Annotation Costs
[CVPR'21] [PDF] [Code]

Sequential Graph Convolutional Network for Active Learning
[CVPR'21] [PDF] [Code]

VaB-AL: Incorporating Class Imbalance and Difficulty With Variational Bayes for Active Learning
[CVPR'21] [PDF] [Code]

Transferable Query Selection for Active Domain Adaptation
[CVPR'21] [PDF] [Code]

Exploring Data-Efficient 3D Scene Understanding With Contrastive Scene Contexts
[CVPR'21] [PDF] [Code]

Task-Aware Variational Adversarial Active Learning
[CVPR'21] [PDF] [Code]

Multiple Instance Active Learning for Object Detection
[CVPR'21] [PDF] [Code]

Deep Active Learning for Biased Datasets via Fisher Kernel Self-Supervision
🕝 [CVPR'20] [PDF] [Code]

State-Relabeling Adversarial Active Learning
[CVPR'20] [PDF] [Code]

ViewAL: Active Learning with Viewpoint Entropy for Semantic Segmentation
[CVPR'20] [PDF] [Code]

Learning Loss for Active Learning
[CVPR'19] [PDF]

Reducing Uncertainty in Undersampled MRI Reconstruction with Active Acquisition
[CVPR'19] [PDF]

The Power of Ensembles for Active Learning in Image Classification
[CVPR'18] [PDF]

Quantization of Fully Convolutional Networks for Accurate Biomedical Image Segmentation
[CVPR'18] [PDF]

Fine-Tuning Convolutional Neural Networks for Biomedical Image Analysis: Actively and Incrementally
[CVPR'17] [PDF] [Code]

HAL3D: Hierarchical Active Learning for Fine-Grained 3D Part Labeling
🕝 [ICCV'23] [PDF]

Hierarchical Point-based Active Learning for Semi-supervised Point Cloud Semantic Segmentation
🕝 [ICCV'23] [PDF] [Code]

ALWOD: Active Learning for Weakly-Supervised Object Detection
🕝 [ICCV'23] [PDF] [Code]

You Never Get a Second Chance To Make a Good First Impression: Seeding Active Learning for 3D Semantic Segmentation
🕝 [ICCV'23] [PDF] [Code]

Heterogeneous Diversity Driven Active Learning for Multi-Object Tracking
🕝 [ICCV'23] [PDF]

TiDAL: Learning Training Dynamics for Active Learning
🕝 [ICCV'23] [PDF] [Code]

Knowledge-Aware Federated Active Learning with Non-IID Data
🕝 [ICCV'23] [PDF] [Code]

Adaptive Superpixel for Active Learning in Semantic Segmentation
[ICCV'23] [PDF] [Code]

Active Universal Domain Adaptation
🕝 [ICCV'21] [PDF]

Semi-Supervised Active Learning for Semi-Supervised Models: Exploit Adversarial Examples With Graph-Based Virtual Labels
🕝 [ICCV'21] [PDF]

Active Learning for Deep Object Detection via Probabilistic Modeling
[ICCV'21] [PDF] [Code]

Contrastive Coding for Active Learning Under Class Distribution Mismatch
[ICCV'21] [PDF] [Code]

Semi-Supervised Active Learning With Temporal Output Discrepancy
[ICCV'21] [PDF] [Code]

Influence Selection for Active Learning
[ICCV'21] [PDF] [Code]

Multi-Anchor Active Domain Adaptation for Semantic Segmentation
[ICCV'21] [PDF] [Code]

Active Learning for Lane Detection: A Knowledge Distillation Approach
[ICCV'21] [PDF]

Active Domain Adaptation via Clustering Uncertainty-Weighted Embeddings
[ICCV'21] [PDF] [Code]

S3VAADA: Submodular Subset Selection for Virtual Adversarial Active Domain Adaptation
[ICCV'21] [PDF] [Code]

LabOR: Labeling Only If Required for Domain Adaptive Semantic Segmentation
[ICCV'21] [PDF]

ReDAL: Region-Based and Diversity-Aware Active Learning for Point Cloud Semantic Segmentation
[ICCV'21] [PDF] [Code]

Active Learning for Deep Detection Neural Networks
[ICCV'19] [PDF] [Code]

Deep Reinforcement Active Learning for Human-in-the-Loop Person Re-Identification
[ICCV'19] [PDF]

Variational Adversarial Active Learning
[ICCV'19] [PDF] [Code]

Computational Evaluation of the Combination of Semi-Supervised and Active Learning for Histopathology Image Segmentation with Missing Annotations
[ICCVW'23] [PDF]

Reducing Label Effort: Self-Supervised Meets Active Learning
[ICCVW'21] [PDF]

Joint semi-supervised and active learning for segmentation of gigapixel pathology images with cost-effective labeling
[ICCVW'21] [PDF]

Learn from the Learnt: Source-Free Active Domain Adaptation via Contrastive Sampling and Visual Persistence
🕝 [ECCV'24] [PDF]

Bad Students Make Great Teachers: Active Learning Accelerates Large-Scale Visual Understanding
🕝 [ECCV'24] [PDF]

Bidirectional Uncertainty-Based Active Learning for Open-Set Annotation
🕝 [ECCV'24] [PDF]

Active Coarse-to-Fine Segmentation of Moveable Parts from Real Images
🕝 [ECCV'24] [PDF]

Efficient Active Domain Adaptation for Semantic Segmentation by Selecting Information-rich Superpixels
🕝 [ECCV'24] [PDF]

Two-Stage Active Learning for Efficient Temporal Action Segmentation
🕝 [ECCV'24] [PDF]

Active Generation for Image Classification
🕝 [ECCV'24] [PDF]

Dataset Quantization with Active Learning based Adaptive Sampling
🕝 [ECCV'24] [PDF]

MetaAT: Active Testing for Label-Efficient Evaluation of Dense Recognition Tasks
🕝 [ECCV'24] [PDF]

Generalized Coverage for More Robust Low-Budget Active Learning
🕝 [ECCV'24] [PDF]

Exploring Active Learning in Meta-Learning: Enhancing Context Set Labeling
🕝 [ECCV'24] [PDF]

Optical Flow Training under Limited Label Budget via Active Learning
🕝 [ECCV'22] [PDF] [Code]

ActiveNeRF: Learning where to See with Uncertainty Estimation
🕝 [ECCV'22] [PDF] [Code]

Active Label Correction Using Robust Parameter Update and Entropy Propagation
🕝 [ECCV'22] [PDF]

LiDAL: Inter-Frame Uncertainty Based Active Learning for 3D LiDAR Semantic Segmentation
[ECCV'22] [PDF] [Code]

Combating Label Distribution Shift for Active Domain Adaptation
[ECCV'22] [PDF] [Code]

Unsupervised Selective Labeling for More Effective Semi-Supervised Learning
[ECCV'22] [PDF] [Code]

D2ADA: Dynamic Density-Aware Active Domain Adaptation for Semantic Segmentation
[ECCV'22] [PDF] [Code]

When Active Learning Meets Implicit Semantic Data Augmentation
[ECCV'22] [PDF]

Talisman: Targeted Active Learning for Object Detection with Rare Classes and Slices Using Submodular Mutual Information
[ECCV'22] [PDF] [Code]

PT4AL: Using Self-Supervised Pretext Tasks for Active Learning
[ECCV'22] [PDF] [Code]

Active learning strategies for weakly-supervised object detection
[ECCV'22] [PDF] [Code]

Active Pointly-Supervised Instance Segmentation
[ECCV'22] [PDF] [Code]

Contextual Diversity for Active Learning
[ECCV'20] [PDF] [Code]

Active Crowd Counting with Limited Supervision
🕝 [ECCV'20] [PDF]

Weight Decay Scheduling and Knowledge Distillation for Active Learning
🕝 [ECCV'20] [PDF]

Consistency-Based Semi-Supervised Active Learning: Towards Minimizing Labeling Cost
[ECCV'20] [PDF]

Two Stream Active Query Suggestion for Active Learning in Connectomics
[ECCV'20] [PDF]

Dual Adversarial Network for Deep Active Learning
[ECCV'20] [PDF]

What do I Annotate Next? An Empirical Study of Active Learning for Action Localization
🕝 [ECCV'18] [PDF]

Class-Balanced Active Learning for Image Classification
[WACV'22] [PDF] [Code]

Active Adversarial Domain Adaptation
[WACV'20] [PDF]

Region-Based Active Learning for Efficient Labeling in Semantic Segmentation
[WACV'19] [PDF]

CEREALS - Cost-Effective REgion-Based Active Learning for Semantic Segmentation
[BMVC'18] [PDF]

A Simple Yet Powerful Deep Active Learning With Snapshots Ensembles
[ICLR'23] [PDF] [Code]

Active Learning for Object Detection with Evidential Deep Learning and Hierarchical Uncertainty Aggregation
[ICLR'23] [PDF] [Code]

Evidential Uncertainty and Diversity Guided Active Learning for Scene Graph Generation
[ICLR'23] [PDF]

Dirichlet-Based Uncertainty Calibration for Active Domain Adaptation
[ICLR'23] [PDF] [Code]

Low-Budget Active Learning via Wasserstein Distance: An Integer Programming Approach
[ICLR'22] [PDF]

Deep Batch Active Learning by Diverse, Uncertain Gradient Lower Bounds
[ICLR'20] [PDF] [Code]

Reinforced Active Learning for Image Segmentation
[ICLR'20] [PDF] [Code]

Active Learning for Convolutional Neural Networks: A Core-Set Approach
[ICLR'18] [PDF] [Code]

Navigating the Pitfalls of Active Learning Evaluation: A Systematic Framework for Meaningful Performance Assessment
[NeurIPS'23] [PDF] [Code]

How to Select Which Active Learning Strategy Is Best Suited for Your Specific Problem and Budget
🕝 [NeurIPS'23] [PDF]

Not All Out-of-Distribution Data Are Harmful to Open-Set Active Learning
🕝 [NeurIPS'23] [PDF] [Code]

Active Learning for Semantic Segmentation with Multi-class Label Query
🕝 [NeurIPS'23] [PDF] [Code]

Annotator: A Generic Active Learning Baseline for LiDAR Semantic Segmentation
🕝 [NeurIPS'23] [PDF] [Code]

AbdomenAtlas-8K: Annotating 8,000 Abdominal CT Volumes for Multi-Organ Segmentation in Three Weeks
[NeurIPS'23] [PDF] [Code]

Towards Free Data Selection with General-Purpose Models
[NeurIPS'23] [PDF] [Code]

A Lagrangian Duality Approach to Active Learning
🕝 [NeurIPS'22] [PDF]

Meta-Query-Net: Resolving Purity-Informativeness Dilemma in Open-set Active Learning
🕝 [NeurIPS'22] [PDF]

Active Learning Helps Pretrained Models Learn the Intended Task
🕝 [NeurIPS'22] [PDF]

Deep Active Learning by Leveraging Training Dynamics
🕝 [NeurIPS'22] [PDF]

Are all Frames Equal? Active Sparse Labeling for Video Action Detection
🕝 [NeurIPS'22] [PDF] [Code]

Active Learning Through a Covering Lens
[NeurIPS'22] [PDF] [Code]

Gone Fishing: Neural Active Learning with Fisher Embeddings
[NeurIPS'21] [PDF] [Code]

Batch Active Learning at Scale
[NeurIPS'21] [PDF]

SIMILAR: Submodular Information Measures Based Active Learning in Realistic Scenarios
[NeurIPS'21] [PDF]

Experimental Design for MRI by Greedy Policy Search
[NeurIPS'20] [PDF] [Code]

BatchBALD: Efficient and Diverse Batch Acquisition for Deep Bayesian Active Learning
[NeurIPS'19] [PDF] [Code]

Active Learning on a Budget: Opposite Strategies Suit High and Low Budgets
[ICML'22] [PDF] [Code]

Bayesian Generative Active Deep Learning
[ICML'19] [PDF] [Code]

Deep Bayesian Active Learning with Image Data
[ICML'17] [PDF]

Density Matters: Improved Core-Set for Active Domain Adaptive Segmentation
🕝 [AAAI'24] [PDF]

EOAL: Entropic Open-set Active Learning
🕝 [AAAI'24] [PDF] [Code]

PRISM: A Rich Class of Parameterized Submodular Information Measures for Guided Subset Selection
[AAAI'22] [PDF]

Boosting Active Learning via Improving Test Performance
[AAAI'22] [PDF] [Code]

Active Learning for Domain Adaptation: An Energy-Based Approach
[AAAI'22] [PDF] [Code]

An Annotation Sparsification Strategy for 3D Medical Image Segmentation via Representative Selection and Self-Training
[AAAI'20] [PDF]

Biomedical Image Segmentation via Representative Annotation
[AAAI'19] [PDF]

Deep Active Learning with Adaptive Acquisition
[IJCAI'19] [PDF] [Code]

Active Label Refinement for Robust Training of Imbalanced Medical Image Classification Tasks in the Presence of High Label Noise
🕝 [MICCAI'24] [PDF]

Adaptive Curriculum Query Strategy for Active Learning in Medical Image Classification
🕝 [MICCAI'24] [PDF]

Advancing UWF-SLO Vessel Segmentation with Source-Free Active Domain Adaptation and a Novel Multi-Center Dataset
🕝 [MICCAI'24] [PDF]

An Uncertainty-guided Tiered Self-training Framework for Active Source-free Domain Adaptation in Prostate Segmentation
🕝 [MICCAI'24] [PDF]

Class-Balancing Deep Active Learning with Auto-Feature Mixing and Minority Push-Pull Sampling
🕝 [MICCAI'24] [PDF]

Few Slices Suffice: Multi-Faceted Consistency Learning with Active Cross-Annotation for Barely-supervised 3D Medical Image Segmentation
🕝 [MICCAI'24] [PDF]

FM-ABS: Promptable Foundation Model Drives Active Barely Supervised Learning for 3D Medical Image Segmentation
🕝 [MICCAI'24] [PDF]

OSAL-ND: Open-set Active Learning for Nucleus Detection
🕝 [MICCAI'24] [PDF]

SBC-AL: Structure and Boundary Consistency-based Active Learning for Medical Image Segmentation
🕝 [MICCAI'24] [PDF]

The MRI Scanner as a Diagnostic: Image-less Active Sampling
🕝 [MICCAI'24] [PDF]

SLPT: Selective Labeling Meets Prompt Tuning on Label-Limited Lesion Segmentation
[MICCAI'23] [PDF]

PLD-AL: Pseudo-label Divergence-Based Active Learning in Carotid Intima-Media Segmentation for Ultrasound Images
[MICCAI'23] [PDF] [Code]

Adaptive Region Selection for Active Learning in Whole Slide Image Semantic Segmentation
[MICCAI'23] [PDF] [Code]

EdgeAL: An Edge Estimation Based Active Learning Approach for OCT Segmentation
[MICCAI'23] [PDF] [Code]

COLosSAL: A Benchmark for Cold-Start Active Learning for 3D Medical Image Segmentation
[MICCAI'23] [PDF] [Code]

atTRACTive: Semi-automatic white matter tract segmentation using active learning
[MICCAI'23] [PDF] [Code]

OpenAL: An Efficient Deep Active Learning Framework for Open-Set Pathology Image Classification
[MICCAI'23] [PDF] [Code]

Discrepancy-Based Active Learning for Weakly Supervised Bleeding Segmentation in Wireless Capsule Endoscopy Images
[MICCAI'22] [PDF]

Consistency-Based Semi-Supervised Evidential Active Learning for Diagnostic Radiograph Classification
[MICCAI'22] [PDF]

Warm Start Active Learning with Proxy Labels and Selection via Semi-Supervised Fine-Tuning
[MICCAI'22] [PDF]

Self-Learning and One-Shot Learning Based Single-Slice Annotation for 3D Medical Image Segmentation
[MICCAI'22] [PDF]

Annotation-Efficient Cell Counting
[MICCAI'21] [PDF] [Code]

Partially-Supervised Learning for Vessel Segmentation in Ocular Images
[MICCAI'21] [PDF]

Quality-Aware Memory Network for Interactive Volumetric Image Segmentation
[MICCAI'21] [PDF] [Code]

Few Is Enough: Task-Augmented Active Meta-learning for Brain Cell Classification
[MICCAI'20] [PDF]

Suggestive Annotation of Brain Tumour Images with Gradient-Guided Sampling
[MICCAI'20] [PDF]

Attention, Suggestion and Annotation: A Deep Active Learning Framework for Biomedical Image Segmentation
[MICCAI'20] [PDF]

Deep Active Learning for Effective Pulmonary Nodule Detection
[MICCAI'20] [PDF]

Learning Guided Electron Microscopy with Active Acquisition
[MICCAI'20] [PDF] [Code]

Active MR K-Space Sampling with Reinforcement Learning
[MICCAI'20] [PDF] [Code]

Deep Active Learning for Breast Cancer Segmentation on Immunohistochemistry Images
[MICCAI'20] [PDF]

Deep Reinforcement Active Learning for Medical Image Classification
[MICCAI'20] [PDF]

Uncertainty-Guided Efficient Interactive Refinement of Fetal Brain Segmentation from Stacks of MRI Slices
[MICCAI'20] [PDF] [Code]

Multiclass Deep Active Learning for Detecting Red Blood Cell Subtypes in Brightfield Microscopy
[MICCAI'19] [PDF]

Cost-Sensitive Active Learning for Intracranial Hemorrhage Detection
[MICCAI'18] [PDF]

Efficient Active Learning for Image Classification and Segmentation Using a Sample Selection and Conditional Generative Adversarial Network
[MICCAI'18] [PDF]

Suggestive Annotation: A Deep Active Learning Framework for Biomedical Image Segmentation
[MICCAI'17] [PDF]

Making Your First Choice: To Address Cold Start Problem in Vision Active Learning
[MIDL'23] [PDF] [Code]

On Learning Adaptive Acquisition Policies for Undersampled Multi-Coil MRI Reconstruction
[MIDL'22] [PDF]

GOAL: Gist-Set Online Active Learning for Efficient Chest X-Ray Image Annotation
[MIDL'21] [PDF]

Active Learning Enhances Classification of Histopathology Whole Slide Images with Attention-Based Multiple Instance Learning
[ISBI'23] [PDF]

Rapid Model Transfer for Medical Image Segmentation Via Iterative Human-in-the-Loop Update: from Labelled Public to Unlabelled Clinical Datasets for Multi-Organ Segmentation in CT
🕝 [ISBI'22] [PDF]

Labeling Cost Sensitive Batch Active Learning For Brain Tumor Segmentation
[ISBI'21] [PDF]

Active Deep Learning with Fisher Information for Patch-Wise Semantic Segmentation
[MICCAIW'18] [PDF]

CLINICAL: Targeted Active Learning for Imbalanced Medical Image Classification
[MICCAIW'22] [PDF]

Mind Your Outliers! Investigating the Negative Impact of Outliers on Active Learning for Visual Question Answering
[ACL'21] [PDF] [Code]

Cold-Start Active Learning through Self-Supervised Language Modeling
[EMNLP'20] [PDF] [Code]

Deep Active Learning: Unified and Principled Method for Query and Training
[AISTATS'20] [PDF] [Code]

Active label cleaning for improved dataset quality under resource constraints
[Nature Communications 2022] [PDF] [Code]

Biological data annotation via a human-augmenting AI-based labeling system
[npj Digital Medicine 2021] [PDF]

ARA: accurate, reliable and active histopathological image classification framework with Bayesian deep learning
🕝 [Scientific Reports 2019] [PDF]

Multiple Instance Differentiation Learning for Active Object Detection
[TPAMI 2023] [PDF] [Code]

Contrastive Active Learning under Class Distribution Mismatch
[TPAMI 2022] [PDF] [Code]

Active Image Synthesis for Efficient Labeling
[TPAMI 2021] [PDF] [Code]

A Probabilistic Active Learning Algorithm Based on Fisher Information Ratio
[TPAMI 2018] [PDF]

Asymptotic analysis of objectives based on fisher information in active learning
[JMLR 2017] [PDF]

ALFREDO: Active Learning with FeatuRe disEntangelement and DOmain adaptation for medical image classification
🕝 [MedIA 2024] [PDF]

Reducing annotating load: Active learning with synthetic images in surgical instrument segmentation
🕝 [MedIA 2024] [PDF]

Deep Bayesian active learning-to-rank with relative annotation for estimation of ulcerative colitis severity
🕝 [MedIA 2024] [PDF]

MONAI label: A framework for ai-assisted interactive labeling of 3d medical images
🕝 [MedIA 2024] [PDF]

Active learning using adaptable task-based prioritisation
🕝 [MedIA 2024] [PDF]

Focused active learning for histopathological image classification
🕝 [MedIA 2024] [PDF]

GANDALF: Graph-based transformer and Data Augmentation Active Learning Framework with interpretable features for multi-label chest Xray classification
[MedIA 2024] [PDF]

Active learning for medical image segmentation with stochastic batches
[MedIA 2023] [PDF] [Code]

HAL-IA: A Hybrid Active Learning framework using Interactive Annotation for medical image segmentation
[MedIA 2023] [PDF]

Deep Active Learning for Suggestive Segmentation of Biomedical Image Stacks via Optimisation of Dice Scores and Traced Boundary Length
[MedIA 2022] [PDF]

Suggestive Annotation of Brain MR Images with Gradient-Guided Sampling
[MedIA 2022] [PDF]

Volumetric Memory Network for Interactive Medical Image Segmentation
[MedIA 2022] [PDF] [Code]

KCB-Net: A 3D Knee Cartilage and Bone Segmentation Network via Sparse Annotation
[MedIA 2022] [PDF]

COVID-AL: The Diagnosis of COVID-19 with Deep Active Learning
[MedIA 2021] [PDF]

Active, Continual Fine Tuning of Convolutional Neural Networks for Reducing Annotation Efforts
[MedIA 2021] [PDF] [Code]

Dual-Reference Source-Free Active Domain Adaptation for Nasopharyngeal Carcinoma Tumor Segmentation across Multiple Hospitals
[TMI 2024] [PDF] [Code]

Learning from Incorrectness: Active Learning with Negative Pre-training and Curriculum Querying for Histological Tissue Classification
[TMI 2023] [PDF] [Code]

Federated Active Learning for Multicenter Collaborative Disease Diagnosis
[TMI 2023] [PDF]

Which Pixel to Annotate: A Label-Efficient Nuclei Segmentation Framework
[TMI 2023] [PDF] [Code]

PathAL: An Active Learning Framework for Histopathology Image Analysis
[TMI 2022] [PDF]

Graph Node Based Interpretability Guided Sample Selection for Active Learning
[TMI 2022] [PDF]

Interpretability-Driven Sample Selection Using Self Supervised Learning for Disease Classification and Segmentation
[TMI 2021] [PDF]

Diminishing Uncertainty within the Training Pool: Active Learning for Medical Image Segmentation
[TMI 2021] [PDF]

Automated Muscle Segmentation from Clinical CT Using Bayesian U-Net for Personalized Musculoskeletal Modeling
[TMI 2020] [PDF]

Rectifying Supporting Regions with Mixed and Active Supervision for Rib Fracture Recognition
[TMI 2020] [PDF]

Intelligent Labeling Based on Fisher Information for Medical Image Segmentation Using Deep Learning
[TMI 2019] [PDF]

DSAL: Deeply Supervised Active Learning from Strong and Weak Labelers for Biomedical Image Segmentation
[JBHI 2021] [PDF] [Code]

Label-Efficient Breast Cancer Histopathological Image Classification
[JBHI 2019] [PDF]

Reliable Label-Efficient Learning for Biomedical Image Recognition
[TBME 2018] [PDF]

Cost-Effective Active Learning for Deep Image Classification
[TCSVT 2017] [PDF]

Cold-Start Active Learning for Image Classification
[Information Sciences 2022] [PDF]

Deep Active Learning Models for Imbalanced Image Classification
[KBS 2022] [PDF]

One-Shot Active Learning for Image Segmentation via Contrastive Learning and Diversity-Based Sampling
[KBS 2022] [PDF]

Active Learning for Segmentation based on Bayesian Sample Queries
[KBS 2021] [PDF] [Code]

Density-Based One-Shot Active Learning for Image Segmentation
[Engineering Applications of Artificial Intelligence 2023] [PDF]

Active Self-Training for Weakly Supervised 3D Scene Semantic Segmentation
[arXiv 2022] [PDF] [Code]

Active CT Reconstruction with a Learned Sampling Policy
[arXiv 2022] [PDF]

A simple baseline for low-budget active learning
[arxiv 2021] [PDF] [Code]

Self-supervised deep active accelerated MRI
[arxiv 2019] [PDF]

Batch Active Learning Using Determinantal Point Processes
[arXiv 2019] [PDF] [Code]

Discriminative Active Learning
[arXiv 2019] [PDF] [Code]

Adversarial Active Learning for Deep Networks: A Margin Based Approach
[arXiv 2018] [PDF]

Generative Adversarial Active Learning
[arXiv 2017] [PDF] [Code]

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