ArticleMedical image analysis2021
Active, continual fine tuning of convolutional neural networks for reducing annotation efforts.
Article in Medical image analysis, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
11 citing papers in PubMed.
- Artificial intelligence for colposcopic and cytological image analysis in early cervical cancer detection.iScience · 2026Review
- SpatialFinder: a human-in-the-loop vision-language framework for prioritizing high-value regions in spatial transcriptomics.Frontiers in bioinformatics · 2026Article
- Rethinking Domain-Specific Pretraining by Supervised or Self-Supervised Learning for Chest Radiograph Classification: A Comparative Study Against ImageNet Counterparts in Cold-Start Active Learning.Health care science · 2025Article
- A deep learning-based radiomics model for predicting lymph node status from lung adenocarcinoma.BMC medical imaging · 2024Article
- Seeking an optimal approach for Computer-aided Diagnosis of Pulmonary Embolism.Medical image analysis · 2024Article
- Active regression model for clinical grading of COVID-19.Frontiers in immunology · 2023Article
- Scellseg: A style-aware deep learning tool for adaptive cell instance segmentation by contrastive fine-tuning.iScience · 2022Article
- A Multimodal Auxiliary Classification System for Osteosarcoma Histopathological Images Based on Deep Active Learning.Healthcare (Basel, Switzerland) · 2022Article
- Deep Active Learning Framework for Lymph Node Metastasis Prediction in Medical Support System.Computational intelligence and neuroscience · 2022Article
- Guest Editorial Annotation-Efficient Deep Learning: The Holy Grail of Medical Imaging.IEEE transactions on medical imaging · 2021Article
- Seeking an Optimal Approach for Computer-Aided Pulmonary Embolism Detection.Machine learning in medical imaging. MLMI (Workshop) · 2021Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
The splendid success of convolutional neural networks (CNNs) in computer vision is largely attributable to the availability of massive annotated datasets, such as ImageNet and Places. However, in medical imaging, it is challenging to create such large annotated datasets, as annotating medical images is not only tedious, laborious, and time consuming, but it also demands costly, specialty-oriented skills, which are not easily accessible. To dramatically reduce annotation cost, this paper presents a novel method to naturally integrate active learning and transfer learning (fine-tuning) into a single framework, which starts directly with a pre-trained CNN to seek "worthy" samples for annotation and gradually enhances the (fine-tuned) CNN via continual fine-tuning. We have evaluated our method using three distinct medical imaging applications, demonstrating that it can reduce annotation efforts by at least half compared with random selection.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.