ArticleJournal of neuro-oncology2023
Histopathological auxiliary system for brain tumour (HAS-Bt) based on weakly supervised learning using a WHO CNS5-style pipeline.
Article in Journal of neuro-oncology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- AI-Powered Histology for Molecular Profiling in Brain Tumors: Toward Smart Diagnostics from Tissue.Cancers · 2025Review
- 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
- Deep learning for multitask prediction on thyroid nodule frozen sections.Frontiers in oncology · 2025Article
- Applications of artificial intelligence in the analysis of histopathology images of gliomas: a review.Npj imaging · 2024Review
- A multi-center performance assessment for automated histopathological classification and grading of glioma using whole slide images.iScience · 2023Article
- Neuropathologist-level integrated classification of adult-type diffuse gliomas using deep learning from whole-slide pathological images.Nature communications · 2023Article
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Authors and funding
13 authors.
Funding
Abstract
purposeClassification and grading of central nervous system (CNS) tumours play a critical role in the clinic. When WHO CNS5 simplifies the histopathology diagnosis and places greater emphasis on molecular pathology, artificial intelligence (AI) has been widely used to meet the increased need for an automatic histopathology scheme that could liberate pathologists from laborious work. This study was to explore the diagnosis scope and practicality of AI.
methodsA one-stop Histopathology Auxiliary System for Brain tumours (HAS-Bt) is introduced based on a pipeline-structured multiple instance learning (pMIL) framework developed with 1,385,163 patches from 1038 hematoxylin and eosin (H&E) slides. The system provides a streamlined service including slide scanning, whole-slide image (WSI) analysis and information management. A logical algorithm is used when molecular profiles are available.
resultsThe pMIL achieved an accuracy of 0.94 in a 9-type classification task on an independent dataset composed of 268 H&E slides. Three auxiliary functions are developed and a built-in decision tree with multiple molecular markers is used to automatically formed integrated diagnosis. The processing efficiency was 443.0 s per slide.
conclusionHAS-Bt shows outstanding performance and provides a novel aid for the integrated neuropathological diagnostic workflow of brain tumours using CNS 5 pipeline.
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