KEYWORDS: Cancer detection, Digital breast tomosynthesis, Data fusion, Breast cancer, Image fusion, Image registration, Education and training, Image processing, Breast, Mammography
PurposeTo develop an artificial intelligence algorithm for the detection of breast cancer by combining upstream data fusion (UDF), machine learning (ML), and automated registration, using digital breast tomosynthesis (DBT) and breast ultrasound (US).ApproachOur retrospective study included examinations from 875 women obtained between April 2013 and January 2019. Included patients had a DBT mammogram, breast US, and biopsy proven breast lesion. Images were annotated by a breast imaging radiologist. An AI algorithm was developed based on ML for image candidate detections and UDF for fused detections. After exclusions, images from 150 patients were evaluated. Ninety-five cases were used for training and validation of ML. Fifty-five cases were included in the UDF test set. UDF performance was evaluated with a free-response receiver operating characteristic (FROC) curve.ResultsForty percent of cases evaluated with UDF (22/55) yielded true ML detections in all three images (craniocaudal DBT, mediolateral oblique DBT, and US). Of these, 20/22 (90.9%) produced a UDF fused detection that contained and classified the lesion correctly. FROC analysis for these cases showed 90% sensitivity at 0.3 false positives per case. In contrast, ML yielded an average of 8.0 false alarms per case.ConclusionsAn AI algorithm combining UDF, ML, and automated registration was developed and applied to test cases, showing that UDF can yield fused detections and decrease false alarms when applied to breast cancer detection. Improvement of ML detection is needed to realize the full benefit of UDF.
KEYWORDS: Digital breast tomosynthesis, Breast cancer, Data fusion, Image fusion, Image processing, Magnetic resonance imaging, Mammography, Image registration, Machine learning, Breast imaging
Machine learning (ML) has made great advancements in imaging for breast cancer detection, including reducing radiologists read times, yet its performance is still reported to be at best similar to that of expert radiologists. This leaves a performance gap between what is desired by radiologists and what can actually be achieved in terms of early detection, reduction of excessive false positives and minimization of unnecessary biopsies. We have seen a similar situation with military intelligence that is expressed by operators as “drowning in data and starving for information”. We invented Upstream Data Fusion (UDF) to help fill the gap. ML is used to produce candidate detections for individual sensing modalities with high detection rates and high false positive rates. Data fusion is used to combine modalities and dramatically diminish false positives. Upstream data, that is closer to raw data, is hard for operators to visualize. Yet it is used for fusion to recover information that would otherwise be lost by the processing to make it visually acceptable to humans. Our research with breast cancer detection involving the fusion of Digital Breast Tomosynthesis (DBT) with Magnetic Resonance Imaging (MRI) and also the fusion of DBT with ultrasound (US) data has yielded preliminary results which lead us to conclude that UDF can help to both fill the performance gap and reduce radiologist read time. Our findings suggest that UDF, combined with ML techniques, can result in paradigm changes in the achievable accuracy and efficiency of early breast cancer detection.
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