ArticleScientific reports2025
Weakly supervised deep learning-based classification for histopathology of gliomas: a single center experience.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Interpretable prototype learning for EGFR amplification prediction from whole-slide images in glioblastoma.Journal of pathology informatics · 2026Article
- Construction of a quantitative extracellular pH map of rat brain glioma using CEST-MRI with iobitridol as the contrast agent.Scientific reports · 2026Article
- Review
- A structure-aware and causal-invariant framework for glioma cell classification in pathological images.Frontiers in cell and developmental biology · 2026Article
- AI-Powered Histology for Molecular Profiling in Brain Tumors: Toward Smart Diagnostics from Tissue.Cancers · 2025Review
- Unsupervised learning of metabolic fingerprints from 3D magnetic resonance spectroscopic imaging enables glioma subtype classification.Neuro-oncology advancesArticle
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Authors and funding
13 authors.
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Abstract
Multiple artificial intelligence systems have been created to facilitate accurate and prompt histopathological diagnosis of tumors using hematoxylin-eosin-stained slides. We aimed to investigate whether weakly supervised deep learning can aid in glioma diagnosis. We analyzed 472 whole slide images (WSIs) from 226 patients in West China Hospital (WCH) and 1604 WSIs from 880 patients in The Cancer Genome Atlas (TCGA). We utilized the OpenSlide library to load WSIs, segmented them into small patches using the DeepZoom module, and then normalized the color using the Reinhard method. A weakly supervised deep learning model was developed using ResNet-50 combined with an attention mechanism. We investigated the performance of the model by calculating area under the curve (AUC) in a ten-fold cross-validation setting. Heatmap visualizations showed the prediction mechanism of the model. The results were promising, with high AUC values for differentiating grades of astrocytomas, oligodendrogliomas, all gliomas, and glioma types in the TCGA dataset (0.9419, 0.8659, 0.9904, and 0.9298, respectively), and in the WCH cohort (0.9048, 0.7423, 0.9510, and 0.7098, respectively). The model demonstrated a strong ability to infer IDH status in the TCGA dataset (AUC = 0.9488). The weakly supervised deep learning model proved to be an effective and reliable tool for neuropathological diagnosis, making it an attractive auxiliary tool.
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Registered trials
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.