SAFARI: shape analysis for AI-segmented images

Fernández, Esteban; Yang, Shengjie; Chiou, Shyh‐Horng; Moon, Chul; Zhang, Cong; Yao, Bo; Xiao, Guanghua; Li, Qiwei

doi:10.1186/s12880-022-00849-8

Cited by 4 publications

(1 citation statement)

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“…The significance of features measuring shape deformations, irregularities, and extremes in medical data analysis is well-documented in various studies, which emphasize their importance over average values for predictive modeling and diagnosis [55][56][57][58][59]. For example, it is found that malignant lesions have rough, irregular surface contours while benign lesions have smooth, regular surfaces, and asymmetry is more common in malignant than benign breast lesions [56,57].…”

Section: Interpretation Of Feature Selection Resultsmentioning

confidence: 99%

A novel feature selection method based on quantum support vector machine

Wang

2024

Phys. Scr.

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Feature selection is critical in machine learning to reduce dimensionality and improve model accuracy and efficiency. The exponential growth in feature space dimensionality for modern datasets directly results in ambiguous samples and redundant features, which can severely degrade classification accuracy. Quantum machine learning offers potential advantages for addressing this challenge. In this paper, we propose a novel method, quantum support vector machine feature selection (QSVMF), integrating quantum support vector machines with multi-objective genetic algorithm. QSVMF optimizes multiple simultaneous objectives: maximizing classification accuracy, minimizing selected features and quantum circuit costs, and reducing feature covariance. We apply QSVMF for feature selection on a breast cancer dataset, comparing the performance of QSVMF against classical approaches with the selected features. Experimental results show that QSVMF achieves superior performance. Furthermore, The Pareto front solutions of QSVMF enable analysis of accuracy versus feature set size trade-offs, identifying extremely sparse yet accurate feature subsets. We contextualize the biological relevance of the selected features in terms of known breast cancer biomarkers. This work highlights the potential of quantum-based feature selection to enhance machine learning efficiency and performance on complex real-world data.

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