ArticleScientific data2024
IPD-Brain: An Indian histopathology dataset for glioma subtype classification.
Article in Scientific data, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Ensemble learning of pathology foundation models for precision oncology.Cancer cell · 2026Article
- AI-driven WHO 2021 classification of gliomas based only on H&E-stained slides.Neuro-oncology · 2026Article
- Multi-Omics Integration for Advancing Glioma Precision Medicine.Annals of clinical and translational neurology · 2026Review
- Contrasting low- and high-resolution features for HER2 scoring using deep learning.Journal of pathology informatics · 2025Article
- IPD-Brain: An Indian histopathology dataset for glioma subtype classification.Scientific data · 2024Article
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Authors and funding
6 authors.
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Abstract
The effective management of brain tumors relies on precise typing, subtyping, and grading. We present the IPD-Brain Dataset, a crucial resource for the neuropathological community, comprising 547 high-resolution H&E stained slides from 367 patients for the study of glioma subtypes and immunohistochemical biomarkers. Scanned at 40x magnification, this dataset is one of the largest in Asia, specifically focusing on the Indian demographics. It encompasses detailed clinical annotations, including patient age, sex, radiological findings, diagnosis, CNS WHO grade, and IHC biomarker status (IDH1R132H, ATRX and TP53 along with proliferation index, Ki67), providing a rich foundation for research. The dataset is open for public access and is designed for various applications, from machine learning model training to the exploration of regional and ethnic disease variations. Preliminary validations utilizing Multiple Instance Learning for tasks such as glioma subtype classification and IHC biomarker identification underscore its potential to significantly contribute to global collaboration in brain tumor research, enhancing diagnostic precision and understanding of glioma variability across different populations.
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