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International Journal of Computer Assisted Radiology and Surgery

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23.12.2023 | Original Article

A semantic fidelity interpretable-assisted decision model for lung nodule classification

verfasst von: Xiangbing Zhan, Huiyun Long, Fangfang Gou, Jia Wu

Erschienen in: International Journal of Computer Assisted Radiology and Surgery | Ausgabe 4/2024

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Abstract

Purpose

Early diagnosis of lung nodules is important for the treatment of lung cancer patients, existing capsule network-based assisted diagnostic models for lung nodule classification have shown promising prospects in terms of interpretability. However, these models lack the ability to draw features robustly at shallow networks, which in turn limits the performance of the models. Therefore, we propose a semantic fidelity capsule encoding and interpretable (SFCEI)-assisted decision model for lung nodule multi-class classification.

Methods

First, we propose multilevel receptive field feature encoding block to capture multi-scale features of lung nodules of different sizes. Second, we embed multilevel receptive field feature encoding blocks in the residual code-and-decode attention layer to extract fine-grained context features. Integrating multi-scale features and contextual features to form semantic fidelity lung nodule attribute capsule representations, which consequently enhances the performance of the model.

Results

We implemented comprehensive experiments on the dataset (LIDC-IDRI) to validate the superiority of the model. The stratified fivefold cross-validation results show that the accuracy (94.17%) of our method exceeds existing advanced approaches in the multi-class classification of malignancy scores for lung nodules.

Conclusion

The experiments confirm that the methodology proposed can effectively capture the multi-scale features and contextual features of lung nodules. It enhances the capability of shallow structure drawing features in capsule networks, which in turn improves the classification performance of malignancy scores. The interpretable model can support the physicians’ confidence in clinical decision-making.

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Titel: A semantic fidelity interpretable-assisted decision model for lung nodule classification
verfasst von: Xiangbing Zhan
Huiyun Long
Fangfang Gou
Jia Wu
Publikationsdatum: 23.12.2023
Verlag: Springer International Publishing
Erschienen in: International Journal of Computer Assisted Radiology and Surgery / Ausgabe 4/2024
Print ISSN: 1861-6410
Elektronische ISSN: 1861-6429
DOI: https://doi.org/10.1007/s11548-023-03043-5

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Abstract

Purpose

Methods

Results

Conclusion

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