A Framework for Brain Tumor Interpretability in Machine Learning for Imaging

A Framework for Brain Tumor Interpretability in Machine Learning for Imaging

Vinit D Katyarkar, Vaishnavi S Talole

Co-Author:  Asst. Prof. M.S.Deore
CSE SIER,Agastkhind Nashik

---------------------------------------------------------------------***---------------------------------------------------------------------

Abstract –

The increasing adoption of machine learning (ML) in medical imaging has highlighted the need for transparent and interpretable models to ensure clinical trust and regulatory compliance. While deep learning systems achieve high diagnostic accuracy, their "black-box" nature raises concerns about reliability in healthcare settings. This paper introduces a novel framework for interpretability in ML-based medical imaging, integrating post-hoc explanation techniques (e.g., saliency maps, attention mechanisms) with inherently interpretable model designs (e.g., decision trees, prototypebased networks). Our framework evaluates interpretability along three dimensions: clinical relevance (alignment with medical reasoning), user accessibility (understandability for non-experts), and faithfulness (accuracy of explanations). Validated on radiology and pathology datasets, the framework demonstrates how interpretability can enhance diagnostic confidence, reduce biases, and facilitate human-AI collaboration. By standardizing interpretability assessment, this work bridges the gap between ML performance and clinical adoption in high-stakes medical applications.

Key Words: Model Evaluation, Medical Imaging, Localization, Trustworthiness, Healthcare AI, Diagnostic Models, Clinical Decision Support