Detection of Breast Cancer with Mammography: Effect of an Artificial Intelligence Support System.

Purpose To compare breast cancer detection performance of radiologists reading mammographic examinations unaided versus supported by an artificial intelligence (AI) system. Materials and Methods An enriched retrospective, fully crossed, multireader, multicase, HIPAA-compliant study was performed. Screening digital mammographic examinations from 240 women (median age, 62 years; range, 39-89 years) performed between 2013 and 2017 were included.

Improving the Automated Detection of Calcifications Using Adaptive Variance Stabilization.

In this paper, we analyze how stabilizing the variance of intensity-dependent quantum noise in digital mammograms can significantly improve the computerized detection of microcalcifications (MCs). These lesions appear on mammograms as tiny deposits of calcium smaller than 20 pixels in diameter. At this scale, high frequency image noise is dominated by quantum noise, which in raw mammograms can be described with a square-root noise model.

Observer variability of reference tissue selection for relativecerebral blood volume measurements in glioma patients.

Objectives: To assess observer variability of different reference tissues used for relative CBV (rCBV) measurements in DSC-MRI of glioma patients.

Methods: In this retrospective study, three observers measured rCBV in DSC-MR images of 44 glioma patients on two occasions. rCBV is calculated by the CBV in the tumour hotspot/the CBV of a reference tissue at the contralateral side for normalization.

The importance of early detection of calcifications associated with breast cancer in screening.

Purpose: The aim of this study was to assess how often women with undetected calcifications in prior screening mammograms are subsequently diagnosed with invasive cancer.

Methods: From a screening cohort of 63,895 women, exams were collected from 59,690 women without any abnormalities, 744 women with a screen-detected cancer and a prior negative exam, 781 women with a false positive exam based on calcifications, and 413 women with an interval cancer. A radiologist identified cancer-related calcifications, selected by a computer-aided detection system, on mammograms taken prior to screen-detected or interval cancer diagnoses.

Improving computer-aided detection assistance in breast cancer screening by removal of obviously false-positive findings.

Purpose: Computer-aided detection (CADe) systems for mammography screening still mark many false positives. This can cause radiologists to lose confidence in CADe, especially when many false positives are obviously not suspicious to them. In this study, we focus on obvious false positives generated by microcalcification detection algorithms.

Reducing false positives of microcalcification detection systems by removal of breast arterial calcifications.

Purpose: In the past decades, computer-aided detection (CADe) systems have been developed to aid screening radiologists in the detection of malignant microcalcifications. These systems are useful to avoid perceptual oversights and can increase the radiologists' detection rate. However, due to the high number of false positives marked by these CADe systems, they are not yet suitable as an independent reader.

Computer-aided detection of breast cancers using Haar-like features in automated 3D breast ultrasound.

Purpose: Automated 3D breast ultrasound (ABUS) has gained interest in breast imaging. Especially for screening women with dense breasts, ABUS appears to be beneficial. However, since the amount of data generated is large, the risk of oversight errors is substantial.