Y Jiang

University of Chicago, Chicago, Illinois, United States

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Publications (11)35.1 Total impact

  • Arthritis Research & Therapy 09/2012; 14(3). · 4.12 Impact Factor
  • [Show abstract] [Hide abstract]
    ABSTRACT: Our purpose was to study the dependence of computer performance in classifying clustered microcalcifications as malignant or benign on the correct detection of microcalcifications. Specifically, we studied the effects of computer-detected true-positive microcalcifications and computer-detected false-positive microcalcifications in true microcalcification clusters. Using a database of 100 mammograms, we compared computer classification performance obtained from computer-detected microcalcifications to (1) computer classification performance obtained from manually identified microcalcifications, and (2) radiologists' performance. When an artificial neural network (ANN) was trained with manually identified microcalcifications, computer classification performance was comparable to or better than radiologists' performance as the number of computer-detected true-positive microcalcifications decreased to 40% and as the number of computer-detected false-positive microcalcifications increased to 50%. Further loss in computer-detected true-positive microcalcifications degraded classification performance substantially. Moreover, training the ANN with computer-detected microcalcifications also degraded computer classification performance. These results show that computer performance in classifying clustered microcalcifications as malignant or benign is insensitive to moderate decreases in computer-detected true-positive microcalcifications and moderate increases in computer-detected false-positive microcalcifications.
    Medical Physics 10/2001; 28(9):1949-57. · 3.01 Impact Factor
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    ABSTRACT: To evaluate whether computer-aided diagnosis can reduce interobserver variability in the interpretation of mammograms. Ten radiologists interpreted mammograms showing clustered microcalcifications in 104 patients. Decisions for biopsy or follow-up were made with and without a computer aid, and these decisions were compared. The computer was used to estimate the likelihood that a microcalcification cluster was due to a malignancy. Variability in the radiologists' recommendations for biopsy versus follow-up was then analyzed. Variation in the radiologists' accuracy, as measured with the SD of the area under the receiver operating characteristic curve, was reduced by 46% with computer aid. Access to the computer aid increased the agreement among all observers from 13% to 32% of the total cases (P <.001), while the kappa value increased from 0.19 to 0.41 (P <.05). Use of computer aid eliminated two-thirds of the substantial disagreements in which two radiologists recommended biopsy and routine screening in the same patient (P <.05). In addition to its demonstrated potential to improve diagnostic accuracy, computer-aided diagnosis has the potential to reduce the variability among radiologists in the interpretation of mammograms.
    Radiology 09/2001; 220(3):787-94. · 6.21 Impact Factor
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    ABSTRACT: Several of the authors have previously published an analysis of multiple sources of uncertainty in the receiver operating characteristic (ROC) assessment and comparison of diagnostic modalities. The analysis assumed that the components of variance were the same for the modalities under comparison. The purpose of the present work is to obtain a generalization that does not require that assumption. The generalization is achieved by splitting three of the six components of variance in the previous model into modality-dependent contributions. Two distinct formulations of this approach can be obtained from alternative choices of the three components to be split; however, a one-to-one relationship exists between the magnitudes of the components estimated from these two formulations. The method is applied to a study of multiple readers, with and without the aid of a computer-assist modality. performing the task of discriminating between benign and malignant clusters of microcalcifications. Analysis according to the first method of splitting shows large decreases in the reader and reader-by-case components of variance when the computer assist is used by the readers. Analysis in terms of the alternative splitting shows large decreases in the corresponding modality-interaction components. A solution to the problem of multivariate ROC analysis without the assumption of equal variance structure across modalities has been provided. Alternative formulations lead to consistent results related by a one-to-one mapping. A surprising result is that estimates of confidence intervals and numbers of cases and readers required for a specified confidence interval remain the same in the more general model as in the restricted model.
    Academic Radiology 08/2001; 8(7):605-15. · 2.08 Impact Factor
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    ABSTRACT: Computer-aided diagnosis (CAD) may be defined as a diagnosis made by a physician who takes into account the computer output as a second opinion. The purpose of CAD is to improve the diagnostic accuracy and the consistency of the radiologists' image interpretation. This article is to provide a brief overview of some of CAD schemes for detection and differential diagnosis of pulmonary nodules and interstitial opacities in chest radiographs as well as clustered micro-calcifications and masses in mammograms. ROC analysis clearly indicated that the radiologists' performances were significantly improved when the computer output was available. An intelligent CAD workstation was developed for detection of breast lesions in mammograms. Results obtained from the first 10,000 cases indicated the potential of CAD in detecting approximately one-half of 'missed' breast cancer.
    European Journal of Radiology 09/1999; 31(2):97-109. · 2.16 Impact Factor
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    ABSTRACT: The purpose of this study was to test whether computer-aided diagnosis (CAD) can improve radiologists' performance in breast cancer diagnosis. The computer classification scheme used in this study estimates the likelihood of malignancy for clustered microcalcifications based on eight computer-extracted features obtained from standard-view mammograms. One hundred four histologically verified cases of microcalcifications (46 malignant, 58 benign) in a near-consecutive biopsy series were used in this study. Observer performance was measured on 10 radiologists who read the original standard- and magnification-view mammograms. The computer aid provided a percentage estimate of the likelihood of malignancy. Comparison was made between computer-aided performance and unaided (routine clinical) performance by using receiver operating characteristic (ROC) analysis and by comparing biopsy recommendations. The average ROC curve area (Az) increased from 0.61 without aid to 0.75 with the computer aid (P < .0001). On average, with the computer aid, each observer recommended 6.4 additional biopsies for cases with malignant lesions (P = .0006) and 6.0 fewer biopsies for cases with benign lesions (P = .003). This improvement corresponded to increases in sensitivity (from 73.5% to 87.4%), specificity (from 31.6% to 41.9%), and hypothetical positive biopsy yield (from 46% to 55%). CAD can be used to improve radiologists' performance in breast cancer diagnosis.
    Academic Radiology 01/1999; 6(1):22-33. · 2.08 Impact Factor
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    ABSTRACT: Area under a receiver operating characteristic (ROC) curve (Az) is widely used as an index of diagnostic performance. However, Az is not a meaningful summary of clinical diagnostic performance when high sensitivity must be maintained clinically. The authors developed a new ROC partial area index, which measures clinical diagnostic performance more meaningfully in such situations, to summarize an ROC curve in only a high-sensitivity region. The mathematical formation of the partial area index was derived from the conventional binormal model. Statistical tests of apparent differences in this index were formulated analogous to that of Az. One common statistical test involving the partial area index was validated by computer simulations under realistic conditions. An example in mammography illustrates a situation in which the partial area index is more meaningful than Az in measuring clinical diagnostic performance. The partial area index can be used as a more meaningful alternative to the conventional Az index for highly sensitive diagnostic tests.
    Radiology 01/1997; 201(3):745-50. · 6.21 Impact Factor
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    ABSTRACT: To develop a method for differentiating malignant from benign clustered microcalcifications in which image features are both extracted and analyzed by a computer. One hundred mammograms from 53 patients who had undergone biopsy for suspicious clustered microcalcifications were analyzed by a computer. Eight computer-extracted features of clustered microcalcifications were merged by an artificial neural network. Human input was limited to initial identification of the microcalcifications. Computer analysis allowed identification of 100% of the patients with breast cancer and 82% of the patients with benign conditions. The accuracy of computer analysis was statistically significantly better than that of five radiologists (P = .03). Quantitative features can be extracted and analyzed by a computer to distinguish malignant from benign clustered microcalcifications. This technique may help radiologists reduce the number of false-positive biopsy findings.
    Radiology 04/1996; 198(3):671-8. · 6.21 Impact Factor
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    ABSTRACT: To improve the performance of a computerized scheme for detection of clustered microcalcifications in digitized mammograms, causes of detected false-positive microcalcification signals were analyzed. The false positives were grouped into four categories, namely, microcalcification like noise patterns, artifacts, linear patterns, and others. In an edge-gradient analysis, local edge-gradient values at signal-perimeter pixels of detected microcalcification signals were determined to eliminate false positives that look like subtle microcalcifications or are due to artifacts. In a linear-pattern analysis, the degree of linearity for linear patterns was determined from local gradient values from a set of linear templates oriented in 16 different directions. Threshold values for the edge-gradient analysis and the linear-pattern analysis were determined using a training database of 39 mammograms. It was possible to eliminate 59% and 25%, respectively, of 91 detected false-positive clusters with loss of only 3% of true-positive clusters. The combination of the two methods further improved the scheme in eliminating a total of 73% of the false-positive clusters with loss of 3% of true-positive clusters. Using these thresholds, the two methods were evaluated on another database of 50 mammograms. 62%, 31%, and 80% of the false-positive clusters were eliminated with loss of 3% of true-positive clusters or less, in the edge-gradient analysis, the linear-pattern analysis, and the combination of the two methods, respectively. The edge-gradient analysis and the linear-pattern analysis can reduce the false-positive detection rate, while maintaining a high level of the sensitivity.
    Medical Physics 03/1995; 22(2):161-9. · 3.01 Impact Factor
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    ABSTRACT: A computerized technique is being developed to automatically detect clustered microcalcifications on digital mammograms. The method consists of three steps. First the signal-to-noise ratio of microcalcifications is enhanced by filtering the image to reduce the normal background structure of the mammogram. Second, signals (potential microcalcifications) are identified by means of global grey-level thresholding, morphological erosion, and a local adaptive grey-level thresholding. Third, the number of falsely detected signals is reduced by examining the power spectrum of individual signals, determining the spatial distribution of the signals, and examining the relationship between size, shape, and background pixel value of microcalcifications. Using this approach, the computer scheme was tested using 78 mammograms, half containing subtle clusters of microcalcifications and half containing no clusters. The scheme was capable of detecting 87% of true clusters with, on average, two false clusters detected per image
    Systems, Man and Cybernetics, 1992., IEEE International Conference on; 11/1992

Publication Stats

665 Citations
35.10 Total Impact Points


  • 1995–2012
    • University of Chicago
      • Department of Radiology
      Chicago, Illinois, United States
  • 2001
    • U.S. Food and Drug Administration
      • Office of Surveillance and Biometrics
      Washington, D. C., DC, United States
  • 1996
    • University of Illinois at Chicago
      • Department of Radiology (Chicago)
      Chicago, IL, United States