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Magnetic Resonance Imaging

Magnetic Resonance Imaging

Magnetic resonance imaging (MRI) is a noninvasive medical imaging technique based on a phenomenon of nuclear physics discovered in the 1930s, in which matter exposed to magnetic fields and radio waves was found to emit radio signals. In 1970, a physician and researcher named Raymond Damadian noticed that malignant (cancerous) tissue gave off different signals than normal body tissue. He applied for a patent for the first MRI scanning device in clinical use by the early 1980s. The early MRI...

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A Framework for Interpretability in Machine Learning for Medical Imaging.

Alan Q Wang^1,2, Batuhan K Karaman^1,2, Heejong Kim²

¹School of Electrical and Computer Engineering, Cornell University-Cornell Tech, New York City, NY 10044, USA.

IEEE Access : Practical Innovations, Open Solutions

|October 18, 2024

Summary

This summary is machine-generated.

Interpretability in machine learning for medical imaging (MLMI) needs formalization. This study defines five core elements and a framework to guide MLMI model design and application for better real-world use.

Keywords:

Interpretability explainability machine learning medical imaging

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Area of Science:

Medical Imaging
Machine Learning
Artificial Intelligence in Healthcare

Background:

Interpretability in machine learning for medical imaging (MLMI) is crucial but lacks clear definition.
Existing approaches often lack a structured understanding of interpretability's goals and components.

Purpose of the Study:

To formalize the goals and elements of interpretability specifically within the MLMI context.
To develop a framework that guides the design and application of interpretable MLMI models.

Main Methods:

Reasoning about real-world medical image analysis tasks and machine learning intersections.
Identifying and defining five core elements of MLMI interpretability: localization, visual recognizability, physical attribution, model transparency, and actionability.

Main Results:

A formalized framework for MLMI interpretability, outlining a step-by-step approach.
Identification of five key elements crucial for practical MLMI interpretability.

Conclusions:

The proposed framework clarifies MLMI-specific interpretability goals and considerations.
This work aims to guide practitioners and researchers in developing and utilizing more interpretable MLMI models for improved clinical impact.