ArticleFrontiers in artificial intelligence2021
A Transfer Learning-Based Active Learning Framework for Brain Tumor Classification.
Article in Frontiers in artificial intelligence, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed.
- Superfast prediction of brain tumours through adaptive incremental pre-training and re-training through transfer learning using state-of-the-art ResNet18 architectural model.Scientific reports · 2026Article
- Artificial Intelligence for Weight Management in Children: A Narrative Review.Healthcare (Basel, Switzerland) · 2026Review
- Artificial intelligence-assisted design and optimization of stimuli-responsive nanocarriers for smart drug delivery.Materials today. Bio · 2026Review
- Retrospection dropout bare-bones particle swarm optimization for feature-based brain tumor classification in MRI images.Frontiers in oncology · 2026Article
- Machine learning-based nomogram for predicting depressive symptoms in women: A cross-sectional study in Guangdong Province, China.World journal of psychiatry · 2025Article
- Challenges and Advances in Classifying Brain Tumors: An Overview of Machine, Deep Learning, and Hybrid Approaches with Future Perspectives in Medical Imaging.Current medical imaging · 2025Review
- Deep superpixel generation and clustering for weakly supervised segmentation of brain tumors in MR images.BMC medical imaging · 2024Article
- Usage of the National Cancer Institute Cancer Research Data Commons by Researchers: A Scoping Review of the Literature.JCO clinical cancer informatics · 2024Article
- Advanced federated ensemble internet of learning approach for cloud based medical healthcare monitoring system.Scientific reports · 2024Article
- Advances in the Use of Deep Learning for the Analysis of Magnetic Resonance Image in Neuro-Oncology.Cancers · 2024Review
- A Review of Recent Advances in Brain Tumor Diagnosis Based on AI-Based Classification.Diagnostics (Basel, Switzerland) · 2023Review
- Deep learning in precision medicine and focus on glioma.Bioengineering & translational medicine · 2023Review
- An active learning approach to train a deep learning algorithm for tumor segmentation from brain MR images.Insights into imaging · 2023Article
- In-Domain Transfer Learning Strategy for Tumor Detection on Brain MRI.Diagnostics (Basel, Switzerland) · 2023Article
- A face image classification method of autistic children based on the two-phase transfer learning.Frontiers in psychology · 2023Article
- Brain tumor classification based on neural architecture search.Scientific reports · 2022Article
- Convolutional Neural Network Techniques for Brain Tumor Classification (from 2015 to 2022): Review, Challenges, and Future Perspectives.Diagnostics (Basel, Switzerland) · 2022Review
- A Data-Efficient Framework for the Identification of Vaginitis Based on Deep Learning.Journal of healthcare engineering · 2022Article
- Radiomics, machine learning, and artificial intelligence-what the neuroradiologist needs to know.Neuroradiology · 2021Review
- A Modified AUC for Training Convolutional Neural Networks: Taking Confidence Into Account.Frontiers in artificial intelligence · 2021Article
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4 authors.
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Abstract
Brain tumor is one of the leading causes of cancer-related death globally among children and adults. Precise classification of brain tumor grade (low-grade and high-grade glioma) at an early stage plays a key role in successful prognosis and treatment planning. With recent advances in deep learning, artificial intelligence-enabled brain tumor grading systems can assist radiologists in the interpretation of medical images within seconds. The performance of deep learning techniques is, however, highly depended on the size of the annotated dataset. It is extremely challenging to label a large quantity of medical images, given the complexity and volume of medical data. In this work, we propose a novel transfer learning-based active learning framework to reduce the annotation cost while maintaining stability and robustness of the model performance for brain tumor classification. In this retrospective research, we employed a 2D slice-based approach to train and fine-tune our model on the magnetic resonance imaging (MRI) training dataset of 203 patients and a validation dataset of 66 patients which was used as the baseline. With our proposed method, the model achieved area under receiver operating characteristic (ROC) curve (AUC) of 82.89% on a separate test dataset of 66 patients, which was 2.92% higher than the baseline AUC while saving at least 40% of labeling cost. In order to further examine the robustness of our method, we created a balanced dataset, which underwent the same procedure. The model achieved AUC of 82% compared with AUC of 78.48% for the baseline, which reassures the robustness and stability of our proposed transfer learning augmented with active learning framework while significantly reducing the size of training data.
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