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  4. Quantitative evaluation of activation maps for weakly-supervised lung nodule segmentation
 
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Quantitative evaluation of activation maps for weakly-supervised lung nodule segmentation

Publikationstyp
Conference Paper
Date Issued
2024-01
Sprache
English
Author(s)
Behrendt, Finn  
Medizintechnische und Intelligente Systeme E-1  
Sonawane, Suyash  
Bhattacharya, Debayan 
Medizintechnische und Intelligente Systeme E-1  
Maack, Lennart  
Medizintechnische und Intelligente Systeme E-1  
Krüger, Julia  
Opfer, Roland  
Schlaefer, Alexander  
Medizintechnische und Intelligente Systeme E-1  
TORE-URI
https://hdl.handle.net/11420/47487
Journal
Progress in Biomedical Optics and Imaging - Proceedings of SPIE  
Volume
12927
Article Number
129272P
Citation
SPIE Medical Imaging 2024
Contribution to Conference
SPIE Medical Imaging 2024  
Publisher DOI
10.1117/12.3006416
Scopus ID
2-s2.0-85191436091
Publisher
SPIE
ISBN
9781510671584
The manual assessment of chest radiographs by radiologists is a time-consuming and error-prone process that relies on the availability of trained professionals. Deep learning methods have the potential to alleviate the workload of radiologists in pathology detection and diagnosis. However, one major drawback of deep learning methods is their lack of explainable decision-making, which is crucial in computer-aided diagnosis. To address this issue, activation maps of the underlying convolutional neural networks (CNN) are frequently used to indicate the regions of focus for the network during predictions. However, often, an evaluation of these activation maps concerning the actual predicted pathology is missing. In this study, we quantitatively evaluate the usage of activation maps for segmenting pulmonary nodules in chest radiographs. We compare transformer-based, CNN-based, and hybrid architectures using different visualization methods. Our results show that although high performance can be achieved in the classification task across all models, the activation masks show little correlation with the actual position of the nodules.
Subjects
Chest Radiographs
Deep Learning
Explainable AI
Nodule Detection
Weakly Supervised Segmentation
MLE@TUHH
DDC Class
610: Medicine, Health
620: Engineering
TUHH
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