Module 1: Glioma Detection
Rapid, AI-driven histopathology analysis to detect or exclude glioma from tissue samples.
Model Result
Based on the model analysis:
Upload WSI for Analysis
Module 3
WSI-Based Glioma Subtype DiagnosisUploaded Image
File Name:
Visual Interpretation of Result
Informative Regions & Morphological Descriptors
The model automatically detects and highlights the most discriminative regions in the Whole Slide Image. Each patch below is accompanied by a morphological descriptor, helping pathologists and researchers to interpret the model’s reasoning.
Morphological Region Insights
AI-Powered Diagnostic Insights from Whole Slide Images
Explore our suite built to accelerate glioma diagnosis*
*Disclaimer: Our solutions assist pathologists by offering rapid, quantitative analyses of histological images. While they enhance workflow efficiency, they are not substitutes for detailed pathological evaluation and should be applied in conjunction with professional expertise.
- Input: Upload a low-resolution WSI (H&E-stained) in jpg/png format.
- Output: Glioma Detected or Glioma Not Detected.
- Accuracy: 95% on internal validation.
This module supports early-stage glioma identification by analyzing histopathological patterns, providing reliable diagnostic clarity. It forms the foundation for subsequent modules focused on detailed subtyping and grading.
Module 2: Glioma Subtype Classification
AI-driven histopathology analysis to classify glioma subtypes.
- Input: Upload a low-resolution WSI (H&E-stained) in jpg/png format.
- Output: Normal, or one of the three glioma subtypes: Astrocytoma, Oligodendroglioma, or Glioblastoma Multiforme (GBM).
- Accuracy: 90% on internal validation.
This module enables glioma subtype identification from images, empowering early clinical insights essential for patient-specific treatment planning. It paves the way for advanced whole-slide analysis in the next module, supporting deeper pathological interpretation from the model.
Module 3: WSI-Based Glioma Subtype Diagnosis
AI-driven whole-slide histopathology to identify and highlight key diagnostic regions.
- Input: Upload a high-resolution WSI (H&E-stained) in svs format.
- Output: Glioma subtype classification with visual interpretation graph and key region descriptors.
- Accuracy: 95% on internal validation.
This module supports glioma subtype diagnosis from WSIs by identifying diagnostically relevant regions, providing visual interpretations, and generating clear, region-specific morphological descriptions. It enables pathologists to efficiently review key areas and make confident clinical decisions.