Explainable artificial intelligence (XAI) in deep learning-based medical image analysis

By Bas H. M. van der Velden, Hugo J. Kuijf, Kenneth G. A. Gilhuijs, Max A. Viergever. Medical Image Anal., 2021

1,073 citations recorded by the source corpus.

With an increase in deep learning-based methods, the call for explainability of such methods grows, especially in high-stakes decision making areas such as medical image analysis. This survey presents an overview of explainable artificial intelligence (XAI) used in deep learning-based medical image analysis. A framework of XAI criteria is introduced to classify deep learning-based medical image analysis methods. Papers on XAI techniques in medical image analysis are then surveyed and categorized according to the framework and according to anatomical location. The paper concludes with an outlook of future opportunities for XAI in medical image analysis.

Subfields: Artificial Intelligence, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/27209d185ffe3c70d9a6a7562c7eb5176750eab7

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Y. Matsui
2. A. Yamada
3. K. Hirata
4. N. Fujima
5. Haisheng Zhu
6. Y. Fushimi
7. S. Naganawa
8. S. Fujita

Citations in the corpus

• Fairness of artificial intelligence in healthcare: review and recommendations (2023, background): “Transparency and accountability are essential elements of ethical AI as they enable healthcare professionals and patients to understand the basis of AI‑generated predictions and foster trust in AI [102].”
• Medical image analysis using deep learning algorithms (2023, background): “When applied to medical image analysis, deep learning algorithms possess the capability to automatically identify and categorize anomalies in various medical images, including X-rays, MRI scans, CT scans, and ultrasound images (10).”