Isaac Leichter

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PURPOSE To evaluate a system for computer-aided classification (CAC) of lesions assigned to Breast Imaging Reporting and Data System (BI-RADS) category 3 at conventional mammographic interpretation. MATERIALS AND METHODS A CAC system was used to analyze 106 cases of lesions (42 malignant) that at blinded retrospective interpretation were assigned to(More)
This paper deals with the multiple annotation problem in medical application of cancer detection in digital images. The main assumption is that though images are labeled by many experts, the number of images read by the same expert is not large. Thus differing with the existing work on modeling each expert and ground truth simultaneously , the multi(More)
In this work are evaluated features selection methods for breast cancer classification in segmented mammographic lesions using two categories of extracted features (numeric and nominal). Numeric included statistical, shape and texture lesion descriptors, and nominal are related with patient's associated metadata descriptors (clinical history). Datasets of(More)
We aimed to determine whether 80 kVp conventional nonenhanced head CT scans have better gray-white matter contrast than standard 120 kVp scans performed on the same patients. Thirty head CT scans acquired at 80 kVp (CT dose index [CTDI]vol 46) were compared to prior studies in the same patients performed at 120 kVp (CTDIvol 59). Signal (Hounsfield units(More)
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