Breast Lesion Detection

Breast lesion detection research focuses on improving the accuracy and efficiency of identifying cancerous and precancerous lesions in mammograms and other breast imaging modalities like tomosynthesis and ultrasound. Current efforts concentrate on developing deep learning models, often employing architectures like feature pyramid networks and attention mechanisms, to analyze images from multiple views and integrate information across different imaging techniques (e.g., combining mammograms and tomosynthesis). These advancements aim to reduce false positives, improve sensitivity, and ultimately enhance early detection rates and patient outcomes, leading to more effective and less anxiety-inducing breast cancer screening.