Details

Autor(en) / Beteiligte

• Thunold, Håvard Horgen
• Riegler, Michael A
• Yazidi, Anis
• Hammer, Hugo L

Titel

A Deep Diagnostic Framework Using Explainable Artificial Intelligence and Clustering

Ist Teil von

• Diagnostics (Basel), 2023-11, Vol.13 (22), p.3413

Ort / Verlag

Basel: MDPI AG

Erscheinungsjahr

2023

Quelle

EZB-FREE-00999 freely available EZB journals

Beschreibungen/Notizen

• An important part of diagnostics is to gain insight into properties that characterize a disease. Machine learning has been used for this purpose, for instance, to identify biomarkers in genomics. However, when patient data are presented as images, identifying properties that characterize a disease becomes far more challenging. A common strategy involves extracting features from the images and analyzing their occurrence in healthy versus pathological images. A limitation of this approach is that the ability to gain new insights into the disease from the data is constrained by the information in the extracted features. Typically, these features are manually extracted by humans, which further limits the potential for new insights. To overcome these limitations, in this paper, we propose a novel framework that provides insights into diseases without relying on handcrafted features or human intervention. Our framework is based on deep learning (DL), explainable artificial intelligence (XAI), and clustering. DL is employed to learn deep patterns, enabling efficient differentiation between healthy and pathological images. Explainable artificial intelligence (XAI) visualizes these patterns, and a novel “explanation-weighted” clustering technique is introduced to gain an overview of these patterns across multiple patients. We applied the method to images from the gastrointestinal tract. In addition to real healthy images and real images of polyps, some of the images had synthetic shapes added to represent other types of pathologies than polyps. The results show that our proposed method was capable of organizing the images based on the reasons they were diagnosed as pathological, achieving high cluster quality and a rand index close to or equal to one.