Strategic analysis reveals strengths and limitations of convolutional neural networks in breast cancer screening, highlighting clinical opportunities and privacy risks.
Key Points
Digital mammography screening enhanced by convolutional neural networks improves breast cancer lesion detection, but clinical integration faces operational hurdles.
Transfer learning and data augmentation strengthen algorithmic accuracy, whereas insufficient data standardisation and low reproducibility remain critical weaknesses.
Clinical deployment offers improved differentiation of microcalcifications, yet automation bias and patient privacy concerns necessitate rigorous clinical judgement.