Biomedical subjects

Yulei Jiang

Publications and source records attributed to Yulei Jiang.

11 recordsLinked to original sources

PubMed2006

Comparison of independent double readings and computer-aided diagnosis (CAD) for the diagnosis of breast calcifications.

RATIONALE AND OBJECTIVES: The aim of the study is to compare independent double readings by radiologists and computer-aided diagnosis (CAD) in diagnostic interpretation of mammographic calcifications. MATERIALS AND METHODS: Ten radiologists independently interpreted 104 mammograms containing clustered microcalcifications. Forty-six of these were malignant and 58 were benign at biopsy. Radiologists read the images with and without a computer aid by using a counterbalanced study design. Sensitivity and specificity were calculated from observer biopsy recommendations, and receiver operating characteristic (ROC) curves were computed from their diagnostic confidence ratings. Unaided double-reading sensitivity and specificity values were derived post hoc by using three different objective rules and an additional rule of simulated-optimal double reading that assumed that consultations for resolving two radiologists' different independent diagnoses always produce the correct clinical recommendation. ROC curves of unaided double readings were obtained according to the literature. RESULTS: Single reading without computer aid yielded 74% sensitivity and 32% specificity, whereas CAD reading yielded 87% sensitivity and 42% specificity and appeared on a higher ROC curve (P < .0001). Three methods of formulating independent double readings generated sensitivities between 59% and 89%, specificities between 50% and 13%, and operating points that moved essentially along the average unaided single-reading ROC curve. ROC curves of unaided independent double readings showed small, statistically insignificant improvement over those of unaided single readings. Results of the simulated-optimal double reading were similar to CAD: 89% sensitivity and 50% specificity. CONCLUSION: Independent double readings of mammographic calcifications may not improve diagnostic performance. CAD reading improves diagnostic performance to an extent approaching the maximum possible performance.

Breast Diseases↗

PubMed2006

A quadratic model for combining quantitative diagnostic assessments from radiologist and computer in computer-aided diagnosis.

RATIONALE AND OBJECTIVES: Some computer-aided diagnosis (CAD) methods produce a quantitative diagnostic assessment (eg, likelihood of malignancy) based on computer image analysis that a radiologist who uses the computer aid must combine with his or her own assessment. Observer studies show that although CAD helps radiologists improve diagnostic performance, ad hoc use of computer aid can produce performance inferior to that of computer alone, indicating that radiologists are unable to incorporate computer assessment optimally into their final assessment. We describe a mathematical model for combining two correlated diagnostic assessments that may provide a basis for merging radiologists' ratings with computer assessments in a way that yields greater diagnostic accuracy than ad hoc merging by radiologists. MATERIALS AND METHODS: We calculate a likelihood ratio from the bivariate binormal model that describes joint probability density functions of latent decision variables of two correlated diagnostic assessments. To the extent that the bivariate binormal model is valid and that the model's parameters can be estimated reliably, results obtained in this way will be optimal because the likelihood ratio is the decision variable used by the ideal observer in any two-group classification task. We evaluated this method on two observer study datasets and in Monte Carlo simulations. RESULTS: This method produced better performance than achieved by radiologists when they incorporated computer assessment in an ad hoc way. Simulations show that with a large number of cases, this method can produce results indistinguishable from the ideal observer performance. CONCLUSIONS: This method can potentially help radiologists use quantitative computed diagnostic assessments optimally, thereby surpassing the computer in accuracy.

Analysis of Variance↗

PubMed2005

A study on several machine-learning methods for classification of malignant and benign clustered microcalcifications.

In this paper, we investigate several state-of-the-art machine-learning methods for automated classification of clustered microcalcifications (MCs). The classifier is part of a computer-aided diagnosis (CADx) scheme that is aimed to assisting radiologists in making more accurate diagnoses of breast cancer on mammograms. The methods we considered were: support vector machine (SVM), kernel Fisher discriminant (KFD), relevance vector machine (RVM), and committee machines (ensemble averaging and AdaBoost), of which most have been developed recently in statistical learning theory. We formulated differentiation of malignant from benign MCs as a supervised learning problem, and applied these learning methods to develop the classification algorithm. As input, these methods used image features automatically extracted from clustered MCs. We tested these methods using a database of 697 clinical mammograms from 386 cases, which included a wide spectrum of difficult-to-classify cases. We analyzed the distribution of the cases in this database using the multidimensional scaling technique, which reveals that in the feature space the malignant cases are not trivially separable from the benign ones. We used receiver operating characteristic (ROC) analysis to evaluate and to compare classification performance by the different methods. In addition, we also investigated how to combine information from multiple-view mammograms of the same case so that the best decision can be made by a classifier. In our experiments, the kernel-based methods (i.e., SVM, KFD, and RVM) yielded the best performance (Az = 0.85, SVM), significantly outperforming a well-established, clinically-proven CADx approach that is based on neural network (Az = 0.80).

Algorithms↗

PubMed2005

Effect of correlation on combining diagnostic information from two images of the same patient.

We have shown previously, in the context of computer-aided diagnosis (CAD), that information derived from multiple images of the same patient can be used to improve diagnostic performance. In that work, we ignored the correlation among multiple images of the same patient. In the present study, we investigate theoretically, within the framework of receiver operating characteristic (ROC) analysis, the effect of correlation on three methods for combining quantitative diagnostic information from two images: taking the average, the maximum, and the minimum of a pair of normally distributed decision variables. We assume, as in our previous work, that the quantitative diagnostic information obtained from the two images of a given patient can be transformed monotonically to two latent decision variables that are normally distributed. Similar to the situation of uncorrelated images, we found that (1) the average always improves the area under the ROC curve (AUC) compared to the single-view image; (2) the maximum and the minimum can also, but not always, improve the AUC; and (3) each method can be the best method in certain situations. In addition, as the correlation strength increases, the average performs the best less often, whereas the maximum and the minimum perform the best more often. These theoretical results are illustrated with analysis of a mammography study.

Area Under Curve↗

PubMed2004

Radial gradient-based segmentation of mammographic microcalcifications: observer evaluation and effect on CAD performance.

Precise segmentation of microcalcifications is essential in the development of accurate mammographic computer-aided diagnosis (CAD) schemes. We have designed a radial gradient-based segmentation method for microcalcifications, and compared it to both the region-growing segmentation method currently used in our CAD scheme and to the watershed segmentation method. Two observer studies were conducted to subjectively evaluate the proposed segmentation method. The first study (A) required observers to rate the segmentation accuracy on a 100-point scale. The second observer evaluation (B) was a preference study in which observers selected their preferred method from three displayed segmentation methods. In study A, the observers gave an average accuracy rating of 88 for the radial gradient-based and 50 for the region-growing segmentation method. In study B, the two observers selected the proposed method 56% and 62% of the time. We also investigated the effect of the proposed segmentation method on the performance of computerized classification scheme in differentiating malignant from benign clustered microcalcifications. The performances of the classification scheme using a linear discriminant analysis (LDA) or a Bayesian artificial neural network classifier both showed statistically significant improvements when using the proposed segmentation method. The areas under the receiver-operating characteristic curves for case-based performance when using the LDA classifier were 0.86 with the proposed segmentation method, 0.80 with the region-growing method, and 0.83 with the watershed method.

Algorithms↗

PubMed2004

An ROC comparison of four methods of combining information from multiple images of the same patient.

Variance of diagnostic information contained in an image degrades diagnostic accuracy. Acquiring multiple images of the same patient (e.g., mediolateral oblique and craniocaudal view mammograms) can, in principle, help reduce this degradation. We demonstrate how this can be accomplished in the context of computer-aided diagnosis (CAD). Assuming that computer outputs obtained from multiple images of the same patient can be transformed monotonically to the same pair of truth-conditional normal distributions and, for simplicity, ignoring correlation among images, we investigate theoretically four methods of combining the computer outputs: taking the average, the median, the maximum, or the minimum. We found, as one would expect, that both the average and the median always produce an improved area under the receiver operating characteristic (ROC) curve (AUC) compared to the single-view images, while the average always produces better performance than the median. However, the maximum and minimum also can produce improved AUCs in some situations, and under certain conditions can outperform the average. Surprisingly, we found that the maximum and minimum of normally-distributed decision variables produce nearly binormal ROC curves. These results can be used as a guide in attempting to increase the efficacy of CAD when multiple images are available from the same patient.

Algorithms↗

PubMed2004

[Effect of emulsion on chromatographic process].

The types and features of concentration wave of the chromatographic process in which the mobile phase was emulsion were studied theoretically by the method of characteristics and the concept of multi mobile phase chromatography. When the mobile phase is an emulsion, solute is partitioned between the inner and outer phases of the emulsion as well as simultaneously adsorbed on the stationary phase resulting in the great difference of concentration profiles from the case in which the mobile phase is homogenous. The various profiles and mechanisms of concentration wave of the chromatographic process in which the partition relationship between the inner and outer phases of the emulsion and the adsorption isotherm on the stationary phase were both Langmuir-like were analyzed and discussed with corresponding examples, and the comparison with the case in which the mobile phase is homogenous is also done. It is shown that the chromatographic progress with emulsion is more complicated and multiple. The existence of multi mobile phases may sharpen simple wave to be shock wave or diffuse shock wave to be simple wave.

Chromatography↗

PubMed2003

Uncertainty in the output of artificial neural networks.

Analysis of the performance of artificial neural networks (ANNs) is usually based on aggregate results on a population of cases. In this paper, we analyze ANN output corresponding to the individual case. We show variability in the outputs of multiple ANNs that are trained and "optimized" from a common set of training cases. We predict this variability from a theoretical standpoint on the basis that multiple ANNs can be optimized to achieve similar overall performance on a population of cases, but produce different outputs for the same individual case because the ANNs use different weights. We use simulations to show that the average standard deviation in the ANN output can be two orders of magnitude higher than the standard deviation in the ANN overall performance measured by the Az value. We further show this variability using an example in mammography where the ANNs are used to classify clustered microcalcifications as malignant or benign based on image features extracted from mammograms. This variability in the ANN output is generally not recognized because a trained individual ANN becomes a deterministic model. Recognition of this variability and the deterministic view of the ANN present a fundamental contradiction. The implication of this variability to the classification task warrants additional study.

Algorithms↗

PubMed2003

The use of a priori information in the detection of mammographic microcalcifications to improve their classification.

In this work, we present a calcification-detection scheme that automatically localizes calcifications in a previously detected cluster in order to generate the input for a cluster-classification scheme developed in the past. The calcification-detection scheme makes use of three pieces of a priori information: the location of the center of the cluster, the size of the cluster, and the approximate number of calcifications in the cluster. This information can be obtained either automatically from a cluster-detection scheme or manually by a radiologist. It is used to analyze only the portion of the mammogram that contains a cluster and to identify the individual calcifications more accurately, after enhancing them by means of a "Difference of Gaussians" filter. Classification performances (patient-based Az=0.92; cluster-based Az=0.72) comparable to those obtained by using manually-identified calcifications (patient-based Az=0.92; cluster-based Az=0.82) can be achieved.

Algorithms↗

PubMed2002

Targeted expression of human vitamin d receptor in the skin promotes the initiation of the postnatal hair follicle cycle and rescues the alopecia in vitamin D receptor null mice.

Alopecia is a predominant feature of vitamin D receptor inactivation in mice and humans. To determine the role of vitamin D receptor in the regulation of hair growth directly, we used the human keratin 14 promoter to target human vitamin D receptor expression to the skin of transgenic mice, and generated vitamin D receptor null mice that express the human vitamin D receptor transgene. Parallel studies were carried out in littermates of wild-type, vitamin D receptor null, transgenic, and human vitamin D receptor-expressing null mice in two transgenic lines. The transgenic mice were grossly normal. The vitamin D receptor null and vitamin D receptor null/human vitamin D receptor mice were growth retarded and developed hypocalcemia, secondary hyperparathyroidism, and rickets. In contrast to the vitamin D receptor null mice that developed alopecia, however, the vitamin D receptor null/human vitamin D receptor mice displayed a normal hair coat, and their hair shaft and skin histology were indistinguishable from those of the wild-type mice. Immunohistochemical analyses revealed that the human vitamin D receptor was highly expressed in the basal layer of the epidermis and outer root sheath of the hair follicle. During follicular morphogenesis, no major histologic differences were seen in the skin of wild-type, vitamin D receptor null, transgenic, and vitamin D receptor null/human vitamin D receptor littermates. When anagen was induced by hair depilation at day 20 after birth, the vitamin D receptor null mice failed to initiate the hair cycle, whereas the vitamin D receptor null/human vitamin D receptor mice displayed the same pattern of anagen follicle formation as the wild-type mice. Interestingly, the transgenic mice initiated the follicular cycle earlier than the wild-type and vitamin D receptor null/human vitamin D receptor mice in a gene concentration-dependent manner. Taken together, these data provide direct evidence that vitamin D receptor is required for the initiation of the postnatal hair follicular cycle in mice.

Alopecia↗

PubMed2002

Computer-aided diagnosis of breast cancer in mammography: evidence and potential.

Computer-aided diagnosis (CAD) methods are being developed to help radiologists improve the interpretation of mammograms for the detection of breast cancer. We review several laboratory observer performance studies of computer-aided diagnosis of malignant and benign breast lesions. These studies show that CAD can improve radiologist diagnostic performance by increasing the number of their biopsy recommendations for actual malignant lesions while reducing the number of their biopsy recommendations for suspicious but actually benign lesions, and by reducing the variability in their interpretation of mammograms. These results indicate a potential clinical role of CAD in mammography for the detection of breast cancer.

Breast Neoplasms↗