ArticleCancers2021
Fine-Tuning Approach for Segmentation of Gliomas in Brain Magnetic Resonance Images with a Machine Learning Method to Normalize Image Differences among Facilities.
Article in Cancers, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers, 2 of them syntheses that pooled it.
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Who cites it
33 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Comparison of Vision Transformers and Convolutional Neural Networks in Medical Image Analysis: A Systematic Review.Journal of medical systems · 2024Pooled it
- Noninvasive grading of glioma brain tumors using magnetic resonance imaging and deep learning methods.Journal of cancer research and clinical oncology · 2023Pooled it
- CT-based automatic segmentation of key CSF regions for detecting disproportionately enlarged subarachnoid space hydrocephalus.Fluids and barriers of the CNS · 2026Article
- Comparing artificial intelligence and physician performance in predicting IDH mutation status in glioma.NPJ digital medicine · 2026Article
- Development and validation of a machine learning model for predicting positive blood cultures using vital signs in ICU patients.Scientific reports · 2026Article
- Distilling population specific expertise into a unified model for generalizable brain tumor segmentation.Scientific reports · 2026Article
- When machines start examining tissue specimens on their own.Neuro-oncology · 2025Article
- Current status and future direction of cancer research using artificial intelligence for clinical application.Cancer science · 2025Review
- Clinical Application of Artificial Intelligence in Ultrasound Imaging for Oncology.JMA journal · 2025Review
- Analysis of Inertial Measurement Unit Data for an AI-Based Physical Function Assessment System Using In-Clinic-like Movements.Bioengineering (Basel, Switzerland) · 2024Article
- Introduction of AI Technology for Objective Physical Function Assessment.Bioengineering (Basel, Switzerland) · 2024Review
- Brain Tumor Detection and Classification Using an Optimized Convolutional Neural Network.Diagnostics (Basel, Switzerland) · 2024Article
- Artificial Intelligence Detection and Segmentation Models: A Systematic Review and Meta-Analysis of Brain Tumors in Magnetic Resonance Imaging.Mayo Clinic proceedings. Digital health · 2024Article
- Applications of Machine Learning (ML) and Mathematical Modeling (MM) in Healthcare with Special Focus on Cancer Prognosis and Anticancer Therapy: Current Status and Challenges.Pharmaceutics · 2024Review
- Automatic assessment of disproportionately enlarged subarachnoid-space hydrocephalus from 3D MRI using two deep learning models.Frontiers in aging neuroscience · 2024Article
- Qualitative MR features to identify non-enhancing tumors within glioblastoma's T2-FLAIR hyperintense lesions.Journal of neuro-oncology · 2023Article
- Analysis of super-enhancer using machine learning and its application to medical biology.Briefings in bioinformatics · 2023Review
- Prediction of cervical lymph node metastasis from immunostained specimens of tongue cancer using a multilayer perceptron neural network.Cancer medicine · 2023Article
- Current role of machine learning and radiogenomics in precision neuro-oncology.Exploration of targeted anti-tumor therapy · 2023Review
- Application of artificial intelligence for automatic cataract staging based on anterior segment images: comparing automatic segmentation approaches to manual segmentation.Frontiers in neuroscience · 2023Article
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28 authors.
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Abstract
Machine learning models for automated magnetic resonance image segmentation may be useful in aiding glioma detection. However, the image differences among facilities cause performance degradation and impede detection. This study proposes a method to solve this issue. We used the data from the Multimodal Brain Tumor Image Segmentation Benchmark (BraTS) and the Japanese cohort (JC) datasets. Three models for tumor segmentation are developed. In our methodology, the BraTS and JC models are trained on the BraTS and JC datasets, respectively, whereas the fine-tuning models are developed from the BraTS model and fine-tuned using the JC dataset. Our results show that the Dice coefficient score of the JC model for the test portion of the JC dataset was 0.779 ± 0.137, whereas that of the BraTS model was lower (0.717 ± 0.207). The mean Dice coefficient score of the fine-tuning model was 0.769 ± 0.138. There was a significant difference between the BraTS and JC models (
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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.