ArticleLife (Basel, Switzerland)2022
AI-Based Pipeline for Classifying Pediatric Medulloblastoma Using Histopathological and Textural Images.
Article in Life (Basel, Switzerland), 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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Who cites it
20 citing papers in PubMed.
- Pediatric brain tumor classification using digital pathology and deep learning: Evaluation of SOTA methods on a multi-center Swedish cohort.Brain pathology (Zurich, Switzerland) · 2026Article
- Integrative Machine Learning Framework for Enhanced Subgroup Classification in Medulloblastoma.Healthcare (Basel, Switzerland) · 2025Article
- Computer-aided diagnosis system for grading brain tumor using histopathology images based on color and texture features.BMC medical imaging · 2024Article
- ADHD-AID: Aiding Tool for Detecting Children's Attention Deficit Hyperactivity Disorder via EEG-Based Multi-Resolution Analysis and Feature Selection.Biomimetics (Basel, Switzerland) · 2024Article
- A multi-class brain tumor grading system based on histopathological images using a hybrid YOLO and RESNET networks.Scientific reports · 2024Article
- Automated Computer-Assisted Medical Decision-Making System Based on Morphological Shape and Skin Thickness Analysis for Asymmetry Detection in Mammographic Images.Diagnostics (Basel, Switzerland) · 2023Article
- A Weakly Supervised Learning Method for Cell Detection and Tracking Using Incomplete Initial Annotations.International journal of molecular sciences · 2023Article
- RiPa-Net: Recognition of Rice Paddy Diseases with Duo-Layers of CNNs Fostered by Feature Transformation and Selection.Biomimetics (Basel, Switzerland) · 2023Article
- Multimodal Classification Framework Based on Hypergraph Latent Relation for End-Stage Renal Disease Associated with Mild Cognitive Impairment.Bioengineering (Basel, Switzerland) · 2023Article
- Deep Learning for Medical Image-Based Cancer Diagnosis.Cancers · 2023Review
- RADIC:A tool for diagnosing COVID-19 from chest CT and X-ray scans using deep learning and quad-radiomics.Chemometrics and intelligent laboratory systems : an international journal sponsored by the Chemometrics Society · 2023Article
- GabROP: Gabor Wavelets-Based CAD for Retinopathy of Prematurity Diagnosis via Convolutional Neural Networks.Diagnostics (Basel, Switzerland) · 2023Article
- MBMethPred: a computational framework for the accurate classification of childhood medulloblastoma subgroups using data integration and AI-based approaches.Frontiers in genetics · 2023Article
- A Framework for Lung and Colon Cancer Diagnosis via Lightweight Deep Learning Models and Transformation Methods.Diagnostics (Basel, Switzerland) · 2022Article
- A wavelet-based deep learning pipeline for efficient COVID-19 diagnosis via CT slices.Applied soft computing · 2022Article
- Unsupervised Learning Based on Multiple Descriptors for WSIs Diagnosis.Diagnostics (Basel, Switzerland) · 2022Article
- Machine-learning approach to predict molecular subgroups of medulloblastoma using multiparametric MRI-based tumor radiomics.The British journal of radiology · 2022Article
- An Intelligent ECG-Based Tool for Diagnosing COVID-19 via Ensemble Deep Learning Techniques.Biosensors · 2022Article
- Radio-pathomic approaches in pediatric neuro-oncology: Opportunities and challenges.Neuro-oncology advancesReview
- A deep learning-based diagnostic tool for identifying various diseases via facial images.Digital healthArticle
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2 authors.
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Abstract
Pediatric medulloblastomas (MBs) are the most common type of malignant brain tumors in children. They are among the most aggressive types of tumors due to their potential for metastasis. Although this disease was initially considered a single disease, pediatric MBs can be considerably heterogeneous. Current MB classification schemes are heavily reliant on histopathology. However, the classification of MB from histopathological images is a manual process that is expensive, time-consuming, and prone to error. Previous studies have classified MB subtypes using a single feature extraction method that was based on either deep learning or textural analysis. Here, we combine textural analysis with deep learning techniques to improve subtype identification using histopathological images from two medical centers. Three state-of-the-art deep learning models were trained with textural images created from two texture analysis methods in addition to the original histopathological images, enabling the proposed pipeline to benefit from both the spatial and textural information of the images. Using a relatively small number of features, we show that our automated pipeline can yield an increase in the accuracy of classification of pediatric MB compared with previously reported methods. A refined classification of pediatric MB subgroups may provide a powerful tool for individualized therapies and identification of children with increased risk of complications.
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