진단 모델 성능 향상을 위한 차원 축소 및 DBSCAN 클러스터링을 사용한 유방촬영술 이상치 탐지

초록

This study introduces a data cleaning technique for identifying and removing anomalous images from mammography data. An autoencoder extracts low-dimensional latent features, which are then refined through dimensionality reduction (methods such as PCA, t-SNE, and Isomap) to highlight irregular patterns. DBSCAN clustering is subsequently employed to detect anomalies. An ablation study confirmed that dimensionality reduction enhances anomaly detection, and the impact of anomaly removal on model training was assessed. Results indicate that the combination of t-SNE and DBSCAN yields superior performance, with the refined model demonstrating significant improvements in accuracy and sensitivity. These findings enhance the reliability of AI-based breast cancer diagnosis and present a promising pre-processing method for medical imaging.

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