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This study evaluates the performance of a new generation algorithm designed to both increase detection sensitivity of cancers and to markedly reduce the false mark rate. In the advanced algorithm, several improvements were implemented. The algorithm for the initial detection of potential mass candidates was upgraded to ignore dense areas that do not represent masses. For the initial detection of potential...
We propose a novel multiple-instance learning (MIL) algorithm for designing classifiers for use in computer aided detection (CAD). The proposed algorithm has 3 advantages over classical methods. First, unlike traditional learning algorithms that minimize the candidate level misclassification error, the proposed algorithm directly optimizes the patient-wise sensitivity. Second, this algorithm automatically...
This study evaluates the performance of an advanced CAD algorithm that is capable of filtering detection marks by level-of-suspicion. The detection of small invasive cancers, which has the greatest impact on breast cancer survival, was investigated. The advanced algorithm (Siemens) permits the radiologist to toggle back and forth between different levels of filtering, and focus on the detection marks...
Computer aided detection systems for mammography typically use standard classification algorithms from machine learning for detecting lesions. However, these general purpose learning algorithms make implicit assumptions that are commonly violated in CAD problems. We propose a new ensemble algorithm that explicitly accounts for the small fraction of outlier images which tend to produce a large number...
16963 FFDM cases (280 cancers), were culled retrospectively and run with a CAD algorithm. Instead of using CAD as a "second reader", the study investigates the feasibility of using CAD for prescreening, allowing cases with no CAD prompts to bypass review, thereby decreasing the workload. The study also investigates the outcome of presorting all cases with matching CAD marks of the same type...
This paper deals with learning spiculation scores of masses in a supervised manner. Three spiculation score prediction models treating the score either as a continuous or ordinary variable are presented. These models were compared on a data-set of 255 masses.
234 pathology-proven FFDM malignant cases and 3872 normal cases were culled retrospectively from 6 screening facilities. For malignant cases, location and size of the biopsied finding and breast density were recorded. All cases were run with a prototype CAD algorithm (Siemens) to evaluate the impact of breast density, lesion size and lesion pathology on CAD performance. The overall CAD sensitivity...
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