A simple method for obtaining artificial 3D forms of 2D mammograms in diagnosis of breast cancer

Toz, Güliz

doi:10.1080/13682199.2023.2235113

Cited by 2 publications

(1 citation statement)

References 40 publications

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“…Models can achieve better prediction performance when considering the spatial depth features of tumors (Ng et al 2013, Ahn et al 2016, Lee et al 2023, Toz 2023. Ng et al (2013) compared the textural feature relationships between maximum cross-sectional area sections and whole tumors by analyzing 5 year overall survival rates.…”

Section: Prediction Models Based On 3d Featuresmentioning

confidence: 99%

An integrated network based on 2D/3D feature correlations for benign-malignant tumor classification and uncertainty estimation in digital breast tomosynthesis

Chen,

Wang,

et al. 2023

Phys. Med. Biol.

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Objective. Classification of benign and malignant tumors is important for the early diagnosis of breast cancer. Over the last decade, digital breast tomosynthesis (DBT) has gradually become an effective imaging modality for breast cancer diagnosis due to its ability to generate three-dimensional (3D) visualizations. However, computer-aided diagnosis (CAD) systems based on 3D images require high computational costs and time. Furthermore, there is considerable redundant information in 3D images. Most CAD systems are designed based on 2D images, which may lose the spatial depth information of tumors. In this study, we propose a 2D/3D integrated network for the diagnosis of benign and malignant breast tumors.Approach. We introduce a correlation strategy to describe feature correlations between slices in 3D volumes, corresponding to the tissue relationship and spatial depth features of tumors. The correlation strategy can be used to extract spatial features with little computational cost. In the prediction stage, 3D spatial correlation features and 2D features are both used for classification.Main results. Experimental results demonstrate that our proposed framework achieves higher accuracy and reliability than pure 2D or 3D models. Our framework has a high area under the curve (AUC) of 0.88 and accuracy of 0.82. The parameter size of the feature extractor in our framework is only 35% of that of the 3D models. In reliability evaluations, our proposed model is more reliable than pure 2D or 3D models because of its effective and nonredundant features.Significance. This study successfully combines 3D spatial correlation features and 2D features for the diagnosis of benign and malignant breast tumors in DBT. In addition to high accuracy and low computational cost, our model is more reliable and can output uncertainty value. From this point of view, the proposed method has the potential to be applied in clinic.

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