Awesome-Active-Learning-for-Medical-Image-Analysis

 

https://github.com/LightersWang/Awesome-Active-Learning-for-Medical-Image-Analysis

@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}
}

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]

IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

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]

International Conference on Computer Vision (ICCV)

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]

ICCV Workshop

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]

European Conference on Computer Vision (ECCV)

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]

IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)

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]

British Machine Vision Conference (BMVC)

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

International Conference on Learning Representations (ICLR)

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]

Advances in Neural Information Processing Systems (NeurIPS)

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]

International Conference on Machine Learning (ICML)

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]

AAAI Conference on Artificial Intelligence (AAAI)

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]

International Joint Conference on Artificial Intelligence (IJCAI)

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

International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI)

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]

International Conference on Medical Imaging with Deep Learning (MIDL)

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]

International Symposium on Biomedical Imaging (ISBI)

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]

MICCAI Workshops

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]

Annual Meeting of the Association for Computational Linguistics (ACL)

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

Conference on Empirical Methods in Natural Language Processing (EMNLP)

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

International Conference on Artificial Intelligence and Statistics (AISTATS)

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

Nature Communications

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

npj Digital Medicine

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

Scientific Reports

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

IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)

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]

Journal of Machine Learning Research (JMLR)

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

Medical Image Analysis (MedIA)

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]

IEEE Transactions on Medical Imaging (TMI)

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]

IEEE Journal of Biomedical and Health Informatics (JBHI)

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]

IEEE Transactions on Biomedical Engineering (TBME)

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

IEEE Transactions on Circuits and Systems for Video Technology (TCSVT)

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

Information Sciences

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

Knowledge-Based Systems (KBS)

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]

Engineering Applications of Artificial Intelligence

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

arXiv

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]