Incorporating Task-Specific Structural Knowledge into CNNs for Brain Midline Shift Detection

Pisov, Maxim; Goncharov, M. A.; Курочкина, Н. С.; Morozov, S. V.; Gombolevskiy, Victor A.; Chernina, Valeria; Vladzymyrskyy, A.V.; Zamyatina, Ksenia; Chesnokova, Anna; Pronin, Igor; Shifrin, Michael; Belyaev, M. Yu.

doi:10.48550/arxiv.1908.04568

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2019

DOI: 10.48550/arxiv.1908.04568

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Incorporating Task-Specific Structural Knowledge into CNNs for Brain Midline Shift Detection

Maxim Pisov¹,

M. A. Goncharov²,

Н. С. Курочкина³

et al.

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2024

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“…Various methods 192 for model interpretability have been proposed in order 193 to address their black-box nature. Approaches such as concept vectors [5,8,28,29] and attention based, perturbation based, and expert knowledge methodologies[27,30] have been explored to improve trust in classification results produced by DNNs. From a clinician perspective, confidence in a classification result is bolstered by model interpretability that provides a clear reason for a decision.…”

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confidence: 99%

LDCT image biomarkers that matter most for the deep learning classification of indeterminate pulmonary nodules

Masquelin,

Cheney,

José Estépar

et al. 2024

CBM

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BACKGROUND: Continued improvement in deep learning methodologies has increased the rate at which deep neural networks are being evaluated for medical applications, including diagnosis of lung cancer. However, there has been limited exploration of the underlying radiological characteristics that the network relies on to identify lung cancer in computed tomography (CT) images. OBJECTIVE: In this study, we used a combination of image masking and saliency activation maps to systematically explore the contributions of both parenchymal and tumor regions in a CT image to the classification of indeterminate lung nodules. METHODS: We selected individuals from the National Lung Screening Trial (NLST) with solid pulmonary nodules 4–20 mm in diameter. Segmentation masks were used to generate three distinct datasets; 1) an Original Dataset containing the complete low-dose CT scans from the NLST, 2) a Parenchyma-Only Dataset in which the tumor regions were covered by a mask, and 3) a Tumor-Only Dataset in which only the tumor regions were included. RESULTS: The Original Dataset significantly outperformed the Parenchyma-Only Dataset and the Tumor-Only Dataset with an AUC of 80.80 ± 3.77% compared to 76.39 ± 3.16% and 78.11 ± 4.32%, respectively. Gradient-weighted class activation mapping (Grad-CAM) of the Original Dataset showed increased attention was being given to the nodule and the tumor-parenchyma boundary when nodules were classified as malignant. This pattern of attention remained unchanged in the case of the Parenchyma-Only Dataset. Nodule size and first-order statistical features of the nodules were significantly different with the average malignant and benign nodule maximum 3d diameter being 23 mm and 12 mm, respectively. CONCLUSION: We conclude that network performance is linked to textural features of nodules such as kurtosis, entropy and intensity, as well as morphological features such as sphericity and diameter. Furthermore, textural features are more positively associated with malignancy than morphological features.

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mentioning

confidence: 99%

LDCT image biomarkers that matter most for the deep learning classification of indeterminate pulmonary nodules

Masquelin,

Cheney,

José Estépar

et al. 2024

CBM

View full text Add to dashboard Cite

show abstract

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Incorporating Task-Specific Structural Knowledge into CNNs for Brain Midline Shift Detection

Cited by 1 publication

References 0 publications

LDCT image biomarkers that matter most for the deep learning classification of indeterminate pulmonary nodules

LDCT image biomarkers that matter most for the deep learning classification of indeterminate pulmonary nodules

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