Computer-Aided Detection Schemes: The Effect of Limiting the Number of Cued Regions in Each Case
Autor: | Gordon S. Abrams, Victor J. Catullo, Betty E. Shindel, Walter F. Good, Bin Zheng, David Gur, Joseph K. Leader |
---|---|
Rok vydání: | 2004 |
Předmět: |
Breast Neoplasms
Sensitivity and Specificity Predictive Value of Tests Humans Medicine Mammography False Positive Reactions Radiology Nuclear Medicine and imaging Diagnosis Computer-Assisted Cued speech Receiver operating characteristic medicine.diagnostic_test business.industry Computer aid Pattern recognition General Medicine Limiting Computer aided detection Radiographic Image Enhancement ROC Curve Female Artificial intelligence Detection rate business Nuclear medicine Sensitivity (electronics) |
Zdroj: | American Journal of Roentgenology. 182:579-583 |
ISSN: | 1546-3141 0361-803X |
Popis: | We assessed performance changes of a mammographic computer-aided detection scheme when we restricted the maximum number of regions that could be identified (cued) as showing positive findings in each case.A computer-aided detection scheme was applied to 500 cases (or 2,000 images), including 300 cases in which mammograms showed verified malignant masses. We evaluated the overall case-based performance of the scheme using a free-response receiver operating characteristic approach, and we measured detection sensitivity at a fixed false-positive detection rate of 0.4 per image after gradually reducing the maximum number of cued regions allowed for each case from seven to one.The original computer-aided detection scheme achieved a maximum case-based sensitivity of 97% at 3.3 false-positive detected regions per image. For a detection decision score set at 0.565, the scheme had a 79% (237/300) case-based sensitivity, with 0.4 false-positive detected regions per image. After limiting the number of maximum allowed cued regions per case, the false-positive rates decreased faster than the true-positive rates. At a maximum of two cued regions per case, the false-positive rate decreased from 0.4 to 0.21 per image, whereas detection sensitivity decreased from 237 to 220 masses. To maintain sensitivity at 79%, we reduced the detection decision score to as low as 0.36, which resulted in a reduction of false-positive detected regions from 0.4 to 0.3 per image and a reduction in region-based sensitivity from 66.1% to 61.4%.Limiting the maximum number of cued regions per case can improve the overall case-based performance of computer-aided detection schemes in mammography. |
Databáze: | OpenAIRE |
Externí odkaz: |