An Interpretable Deep Hierarchical Semantic Convolutional Neural Network for Lung Nodule Malignancy Classification

While deep learning methods have demonstrated performance comparable to human readers in tasks such as computer-aided diagnosis, these models are difficult to interpret, do not incorporate prior domain knowledge, and are often considered as a "black-box." The lack of model interpretability...

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Bibliographische Detailangaben

Veröffentlicht in:	Expert systems with applications. - 1999. - 128(2019) vom: 15. Aug., Seite 84-95
1. Verfasser:	Shen, Shiwen (VerfasserIn)
Weitere Verfasser:	Han, Simon X, Aberle, Denise R, Bui, Alex A, Hsu, William
Format:	Online-Aufsatz
Sprache:	English
Veröffentlicht:	2019
Zugriff auf das übergeordnete Werk:	Expert systems with applications
Schlagworte:	Journal Article Computed tomography Lung nodule classification convolutional neural networks deep learning lung cancer diagnosis model interpretability