Computer-Aided Diagnosis System for Lung Fibrosis: From the Effect of Radiomic Features and Multi-Layer-Perceptron Mixers to Pre-Clinical Evaluation

Fontanellaz, M.; Christe, A.; Christodoulidis, S.; Dack, E.; Roos, J.; Drakopoulos, D.; Sieron, D.; Peters, A.; Geiser, T.; Funke-Chambour, M.; Heverhagen, J.; Hoppe, H.; Exadaktylos, A. K.; Ebner, L.; Mougiakakou, S. (2024). Computer-Aided Diagnosis System for Lung Fibrosis: From the Effect of Radiomic Features and Multi-Layer-Perceptron Mixers to Pre-Clinical Evaluation. IEEE access, 12, pp. 25642-25656. IEEE 10.1109/ACCESS.2024.3350430

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Medical image segmentation is a crucial element of computer-aided diagnosis (CAD) systems. Segmentation maps are used to calculate imaging features, such as quantitative disease distribution and radiomic features. Since their introduction in 2015, UNets have become the state-of-the-art segmentation tools. However, since that time, many new methods for image processing have been introduced, such as vision transformers and multi-layer-perceptron-mixers (MLP-Mixers). Alongside baseline UNets, we have now investigated the application of such MLP-Mixers for medical image segmentation, as part of a CAD system for the diagnosis of interstitial lung diseases (ILDs). Furthermore, we have investigated the effect of 2D and 3D data representations on segmentation and the final CAD results. We have evaluated the performance of the baseline segmentation methods and the MLP-Mixer primary on the overall diagnostic performance of the CAD system - as well as on the accuracy of segmentation as an intermediate step. In addition to network and data representation variations, we have investigated two different techniques for selecting features, an agnostic method and an alternative approach which selects features tailored to a specific segmentation map and diagnosis task. Finally, the CAD’s performance was compared with that of four independent specialists in chest radiology. Among the 105 test cases, the diagnostic accuracy was 77.2±1.6% for the AI-approaches and 79.0±6.9% for the radiologists, indicating that the proposed systems perform comparably well to human readers in most of the cases. For the task of ILD pattern segmentation, similar results were obtained with 3D data and 2D tomography slices.

Item Type:

Journal Article (Original Article)


04 Faculty of Medicine > Faculty Institutions > sitem Center for Translational Medicine and Biomedical Entrepreneurship
04 Faculty of Medicine > Department of Intensive Care, Emergency Medicine and Anaesthesiology (DINA) > University Emergency Center
04 Faculty of Medicine > Department of Gastro-intestinal, Liver and Lung Disorders (DMLL) > Clinic of Pneumology
04 Faculty of Medicine > Department of Radiology, Neuroradiology and Nuclear Medicine (DRNN) > Institute of Diagnostic, Interventional and Paediatric Radiology
10 Strategic Research Centers > ARTORG Center for Biomedical Engineering Research > ARTORG Center - AI in Health and Nutrition

UniBE Contributor:

Fontanellaz, Matthias Andreas, Christe, Andreas, Dack, Ethan Lowell Thorpe, Drakopoulos, Dionysios, Peters, Alan Arthur, Geiser, Thomas (A), Funke-Chambour, Manuela, Heverhagen, Johannes, Hoppe, Hanno, Exadaktylos, Aristomenis, Ebner, Lukas, Mougiakakou, Stavroula


600 Technology > 610 Medicine & health








Maria de Fatima Henriques Bernardo

Date Deposited:

14 Mar 2024 13:10

Last Modified:

14 Mar 2024 13:10

Publisher DOI:





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