ArticleBiomedical engineering letters2018
Automatic disease stage classification of glioblastoma multiforme histopathological images using deep convolutional neural network.
Article in Biomedical engineering letters, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
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Who cites it
26 citing papers in PubMed, 56 citations in OpenAlex.
- MANet: a multimodal attention convolutional neural network for brain tumor classification.Scientific reports · 2026Article
- End-to-End Multimodal Multiple Instance Learning for Cancer Histopathology Classification with Dual-Attention Fusion.Journal of medical systems · 2026Article
- Cross-Architecture Knowledge Distillation for Histopathological Image Analysis.IEEE access : practical innovations, open solutions · 2026Article
- A structure-aware and causal-invariant framework for glioma cell classification in pathological images.Frontiers in cell and developmental biology · 2026Article
- Histopathological-based brain tumor grading using 2D-3D multi-modal CNN-transformer combined with stacking classifiers.Scientific reports · 2025Article
- Improved lung nodule segmentation with a squeeze excitation dilated attention based residual UNet.Scientific reports · 2025Article
- Context aware machine learning techniques for brain tumor classification and detection - A review.Heliyon · 2025Review
- Multimodal Ensemble Fusion Deep Learning Using Histopathological Images and Clinical Data for Glioma Subtype Classification.IEEE access : practical innovations, open solutions · 2025Article
- Deep learning-based IDH1 gene mutation prediction using histopathological imaging and clinical data.Computers in biology and medicine · 2024Article
- Applications of artificial intelligence in the analysis of histopathology images of gliomas: a review.Npj imaging · 2024Review
- A multi-class brain tumor grading system based on histopathological images using a hybrid YOLO and RESNET networks.Scientific reports · 2024Article
- A multi-center performance assessment for automated histopathological classification and grading of glioma using whole slide images.iScience · 2023Article
- Histopathological auxiliary system for brain tumour (HAS-Bt) based on weakly supervised learning using a WHO CNS5-style pipeline.Journal of neuro-oncology · 2023Article
- The "Superoncogene" Myc at the Crossroad between Metabolism and Gene Expression in Glioblastoma Multiforme.International journal of molecular sciences · 2023Review
- Artificial Intelligence and Precision Medicine: A New Frontier for the Treatment of Brain Tumors.Life (Basel, Switzerland) · 2022Review
- A 3-miRNA Signature Enables Risk Stratification in Glioblastoma Multiforme Patients with Different Clinical Outcomes.Current oncology (Toronto, Ont.) · 2022Article
- Review
- Real-time intraoperative glioma diagnosis using fluorescence imaging and deep convolutional neural networks.European journal of nuclear medicine and molecular imaging · 2021Article
- Are radiomics features universally applicable to different organs?Cancer imaging : the official publication of the International Cancer Imaging Society · 2021Article
- Can artificial intelligence overtake human intelligence on the bumpy road towards glioma therapy?Medical oncology (Northwood, London, England) · 2021Review
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In the field of computational histopathology, computer-assisted diagnosis systems are important in obtaining patient-specific diagnosis for various diseases and help precision medicine. Therefore, many studies on automatic analysis methods for digital pathology images have been reported. In this work, we discuss an automatic feature extraction and disease stage classification method for glioblastoma multiforme (GBM) histopathological images. In this paper, we use deep convolutional neural networks (Deep CNNs) to acquire feature descriptors and a classification scheme simultaneously. Further, comparisons with other popular CNNs objectively as well as quantitatively in this challenging classification problem is undertaken. The experiments using Glioma images from The Cancer Genome Atlas shows that we obtain
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