ArticleSensors (Basel, Switzerland)2021
Classification of Diffuse Glioma Subtype from Clinical-Grade Pathological Images Using Deep Transfer Learning.
Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 1 of them a synthesis that pooled it.
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Who cites it
20 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Deep learning for detecting early gastric cancer with white-light endoscopy: a systematic review and meta-analysis.Frontiers in artificial intelligence · 2026Pooled it
- Histopathological-based brain tumor grading using 2D-3D multi-modal CNN-transformer combined with stacking classifiers.Scientific reports · 2025Article
- Weakly supervised deep learning-based classification for histopathology of gliomas: a single center experience.Scientific reports · 2025Article
- Brain tumor intelligent diagnosis based on Auto-Encoder and U-Net feature extraction.PloS one · 2025Article
- Multimodal Ensemble Fusion Deep Learning Using Histopathological Images and Clinical Data for Glioma Subtype Classification.IEEE access : practical innovations, open solutions · 2025Article
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- Applications of artificial intelligence in the analysis of histopathology images of gliomas: a review.Npj imaging · 2024Review
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- A multi-class brain tumor grading system based on histopathological images using a hybrid YOLO and RESNET networks.Scientific reports · 2024Article
- Deep Learning for the Pathologic Diagnosis of Hepatocellular Carcinoma, Cholangiocarcinoma, and Metastatic Colorectal Cancer.Cancers · 2023Article
- Dysregulation of inflammasome activation in glioma.Cell communication and signaling : CCS · 2023Review
- Non-annotated renal histopathological image analysis with deep ensemble learning.Quantitative imaging in medicine and surgery · 2023Article
- Contribution of whole slide imaging-based deep learning in the assessment of intraoperative and postoperative sections in neuropathology.Brain pathology (Zurich, Switzerland) · 2023Article
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- Artificial intelligence in neuro-oncology.Frontiers in neuroscience · 2023Review
- The use of artificial intelligence for delivery of essential health services across WHO regions: a scoping review.Frontiers in public health · 2023Article
- Application of Artificial Intelligence in Pathology: Trends and Challenges.Diagnostics (Basel, Switzerland) · 2022Review
- An update on the molecular biology of glioblastoma, with clinical implications and progress in its treatment.Cancer communications (London, England) · 2022Review
- Brain Tumor Imaging: Applications of Artificial Intelligence.Seminars in ultrasound, CT, and MR · 2022Article
- Direct image to subtype prediction for brain tumors using deep learning.Neuro-oncology advancesArticle
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Authors and funding
7 authors.
Funding
Abstract
Diffuse gliomas are the most common primary brain tumors and they vary considerably in their morphology, location, genetic alterations, and response to therapy. In 2016, the World Health Organization (WHO) provided new guidelines for making an integrated diagnosis that incorporates both morphologic and molecular features to diffuse gliomas. In this study, we demonstrate how deep learning approaches can be used for an automatic classification of glioma subtypes and grading using whole-slide images that were obtained from routine clinical practice. A deep transfer learning method using the ResNet50V2 model was trained to classify subtypes and grades of diffuse gliomas according to the WHO's new 2016 classification. The balanced accuracy of the diffuse glioma subtype classification model with majority voting was 0.8727. These results highlight an emerging role of deep learning in the future practice of pathologic diagnosis.
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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.