ArticleScientific reports2022
A weakly supervised deep learning-based method for glioma subtype classification using WSI and mpMRIs.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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20 citing papers in PubMed.
- Multimodal fusion of pathology and radiology foundation models for WHO 2021 glioma subtyping.NPJ precision oncology · 2026Article
- Bridging modalities with AI: a review of AI advances in multimodal biomedical imaging.Communications engineering · 2026Review
- Development and validation of radiopathomics models for predicting molecular subtypes and WHO grades in adult-type diffuse gliomas: a multicenter study.Journal of translational medicine · 2025Article
- Artificial Intelligence in the Diagnosis and Treatment of Brain Gliomas.Biomedicines · 2025Review
- Enhancing Interstitial Lung Disease Diagnoses Through Multimodal AI Integration of Histopathological and CT Image Data.Respirology (Carlton, Vic.) · 2025Article
- Majority Voting Ensemble of Deep CNNs for Robust MRI-Based Brain Tumor Classification.Diagnostics (Basel, Switzerland) · 2025Article
- Weakly supervised deep learning-based classification for histopathology of gliomas: a single center experience.Scientific reports · 2025Article
- Computer-aided diagnosis system for grading brain tumor using histopathology images based on color and texture features.BMC medical imaging · 2024Article
- Applications of artificial intelligence in the analysis of histopathology images of gliomas: a review.Npj imaging · 2024Review
- Study of radiochemotherapy decision-making for young high-risk low-grade glioma patients using a macroscopic and microscopic combined radiomics model.European radiology · 2024Article
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- Optimizing Storage and Computational Efficiency: An Efficient Algorithm for Whole Slide Image Size Reduction.Mayo Clinic proceedings. Digital health · 2023Article
- Multiparametric MRI: From Simultaneous Rapid Acquisition Methods and Analysis Techniques Using Scoring, Machine Learning, Radiomics, and Deep Learning to the Generation of Novel Metrics.Investigative radiology · 2023Review
- Contribution of whole slide imaging-based deep learning in the assessment of intraoperative and postoperative sections in neuropathology.Brain pathology (Zurich, Switzerland) · 2023Article
- Preanalytic variable effects on segmentation and quantification machine learning algorithms for amyloid-β analyses on digitized human brain slides.Journal of neuropathology and experimental neurology · 2023Article
- A novel federated deep learning scheme for glioma and its subtype classification.Frontiers in neuroscience · 2023Article
- MAC-ResNet: Knowledge Distillation Based Lightweight Multiscale-Attention-Crop-ResNet for Eyelid Tumors Detection and Classification.Journal of personalized medicine · 2022Article
- Radio-pathomic approaches in pediatric neuro-oncology: Opportunities and challenges.Neuro-oncology advancesReview
- Uses of artificial intelligence in glioma: A systematic review.Medicine internationalArticle
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8 authors.
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Abstract
Accurate glioma subtype classification is critical for the treatment management of patients with brain tumors. Developing an automatically computer-aided algorithm for glioma subtype classification is challenging due to many factors. One of the difficulties is the label constraint. Specifically, each case is simply labeled the glioma subtype without precise annotations of lesion regions information. In this paper, we propose a novel hybrid fully convolutional neural network (CNN)-based method for glioma subtype classification using both whole slide imaging (WSI) and multiparametric magnetic resonance imagings (mpMRIs). It is comprised of two methods: a WSI-based method and a mpMRIs-based method. For the WSI-based method, we categorize the glioma subtype using a 2D CNN on WSIs. To overcome the label constraint issue, we extract the truly representative patches for the glioma subtype classification in a weakly supervised fashion. For the mpMRIs-based method, we develop a 3D CNN-based method by analyzing the mpMRIs. The mpMRIs-based method consists of brain tumor segmentation and classification. Finally, to enhance the robustness of the predictions, we fuse the WSI-based and mpMRIs-based results guided by a confidence index. The experimental results on the validation dataset in the competition of CPM-RadPath 2020 show the comprehensive judgments from both two modalities can achieve better performance than the ones by solely using WSI or mpMRIs. Furthermore, our result using the proposed method ranks the third place in the CPM-RadPath 2020 in the testing phase. The proposed method demonstrates a competitive performance, which is creditable to the success of weakly supervised approach and the strategy of label agreement from multi-modality data.
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