ArticlePloS one2016
Retrieval of Brain Tumors by Adaptive Spatial Pooling and Fisher Vector Representation.
Article in PloS one, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 43 papers.
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Who cites it
43 citing papers in PubMed.
- YOLO-LS: a novel deep learning framework for brain tumor segmentation in Magnetic Resonance Imaging.Scientific reports · 2026Article
- Deep Learning for Content-Based Medical Image Retrieval in Picture Archiving and Communication Systems for Brain Tumor Detection: Algorithm Development and Validation.JMIR medical informatics · 2026Article
- Dual-path deep learning framework for accurate and interpretable brain tumor diagnosis.BMC medical informatics and decision making · 2026Article
- Integrating artificial intelligence in healthcare: applications, challenges, and future directions.Future science OA · 2025Review
- Efficient Brain Tumor Segmentation for MRI Images Using YOLO-BT.Sensors (Basel, Switzerland) · 2025Article
- Patch-Based Texture Feature Extraction Towards Improved Clinical Task Performance.Bioengineering (Basel, Switzerland) · 2025Article
- Adaptive image encryption approach using an enhanced swarm intelligence algorithm.Scientific reports · 2025Article
- CerevianNet: parameter efficient multi-class brain tumor classification using custom lightweight CNN.Frontiers in medicine · 2025Article
- GT-Net: global transformer network for multiclass brain tumor classification using MR images.Biomedical engineering letters · 2024Article
- MedCapsNet: A modified Densenet201 model integrated with capsule network for heel disease detection and classification.Heliyon · 2024Article
- A Novel Hybrid Machine Learning-Based System Using Deep Learning Techniques and Meta-Heuristic Algorithms for Various Medical Datatypes Classification.Diagnostics (Basel, Switzerland) · 2024Article
- Advancing brain tumor classification through MTAP model: an innovative approach in medical diagnostics.Medical & biological engineering & computing · 2024Article
- Advanced image generation for cancer using diffusion models.Biology methods & protocols · 2024Article
- Brain tumor classification from MRI scans: a framework of hybrid deep learning model with Bayesian optimization and quantum theory-based marine predator algorithm.Frontiers in oncology · 2024Article
- A fine-tuned vision transformer based enhanced multi-class brain tumor classification using MRI scan imagery.Frontiers in oncology · 2024Article
- Article
- Deep-learning model for diagnostic clue: detecting the dural tail sign for meningiomas on contrast-enhanced T1 weighted images.Quantitative imaging in medicine and surgery · 2023Article
- MISM: A Medical Image Segmentation Metric for Evaluation of Weak Labeled Data.Diagnostics (Basel, Switzerland) · 2023Article
- Article
- Refined Automatic Brain Tumor Classification Using Hybrid Convolutional Neural Networks for MRI Scans.Diagnostics (Basel, Switzerland) · 2023Article
Corrections and comments
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Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Content-based image retrieval (CBIR) techniques have currently gained increasing popularity in the medical field because they can use numerous and valuable archived images to support clinical decisions. In this paper, we concentrate on developing a CBIR system for retrieving brain tumors in T1-weighted contrast-enhanced MRI images. Specifically, when the user roughly outlines the tumor region of a query image, brain tumor images in the database of the same pathological type are expected to be returned. We propose a novel feature extraction framework to improve the retrieval performance. The proposed framework consists of three steps. First, we augment the tumor region and use the augmented tumor region as the region of interest to incorporate informative contextual information. Second, the augmented tumor region is split into subregions by an adaptive spatial division method based on intensity orders; within each subregion, we extract raw image patches as local features. Third, we apply the Fisher kernel framework to aggregate the local features of each subregion into a respective single vector representation and concatenate these per-subregion vector representations to obtain an image-level signature. After feature extraction, a closed-form metric learning algorithm is applied to measure the similarity between the query image and database images. Extensive experiments are conducted on a large dataset of 3604 images with three types of brain tumors, namely, meningiomas, gliomas, and pituitary tumors. The mean average precision can reach 94.68%. Experimental results demonstrate the power of the proposed algorithm against some related state-of-the-art methods on the same dataset.
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