Interpretable machine learning for brain tumour analysis using MRI and whole slide images
Sasmitha Dasanayaka, Vimuth Shantha, Sanju Silva, Dulani Meedeniya, Thanuja D. Ambegoda
Abstract
Tumour-Analyser is a web application that classifies a brain tumour into three classes, namely, lower-grade astrocytoma (A), oligodendroglioma (O), glioblastoma & diffuse astrocytic glioma (G). We use a magnetic resonance imaging (MRI) sequence and a whole slide imaging (WSI) that are classified using DenseNet and ResNet, respectively. The tool interprets the decision-making process of each classification model. Tumour-Analyser provides a viable solution to the less human understandability of existing models due to the inherent black-box nature of deep learning models and less transparency, by applying interpretability.
Topics & Concepts
InterpretabilityAnalyserComputer scienceArtificial intelligenceAstrocytomaMagnetic resonance imagingDeep learningOligodendrogliomaPreprocessorGliomaPattern recognition (psychology)MedicineRadiologyChemistryCancer researchChromatographyExplainable Artificial Intelligence (XAI)Radiomics and Machine Learning in Medical ImagingBrain Tumor Detection and Classification