Evidence
Robust Breast Cancer Detection in Mammography and Digital Breast Tomosynthesis Using an Annotation-Efficient Deep Learning Approach
Nature Medicine
Lotter et al. Jan 2021
DeepHealth’s deep-learning algorithm achieves state of the art performance in mammogram classification. The AI model was compared to 5 readers in a reader study of 131 index cancers and 154 confirmed negatives. The model showed robust and generalizable performance, reporting an Area Under the Curve (AUC) of 0.945 and outperforming all radiologists with a sensitivity 14% higher than the average radiologist sensitivity.
Validation, Comparison, and Combination of Algorithms for Automatic Detection of Pulmonary Nodules in Computed Tomography Images: The LUNA16 Challenge
Medical Image Analysis
Setio et al. Dec 2017
The LUNA16 challenge is an objective evaluation framework for automatic nodule detection algorithms using the largest publicly available reference database of chest CT scans. Participants developed algorithms focused on 1) the complete nodule detection track or 2) the false positive reduction track. The impact of combined systems was also investigated. The combination of these solutions achieved a sensitivity of over 95% at fewer than 1.0 false positives per scan, highlighting the potential to improve detection performance.