Retrieval boosted computer-aided diagnosis of clustered microcalcifications for breast cancer

Jing, Hao; Yang, Yongyi; Nishikawa, Robert M.

doi:10.1118/1.3675600

Cited by 12 publications

(23 citation statements)

References 57 publications

(78 reference statements)

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“…To motivate the proposed development, below we first briefly review the case-adaptive approach developed previously in [22]. For simplicity, our approach will be presented using a linear classifier.…”

Section: Regularized Adaptive Classification With Retrieval Of Simmentioning

confidence: 99%

“…In [22], we considered logistic regression [26], in which w is determined by maximizing the following log-likelihood function:

\begin{matrix} L (boldw) = {false\sum}_{i = 1}^{N} \log p (y_{i}, {boldx}_{i}; boldw), \end{matrix}

where p ( y i = 1, x i ; w ) = [1+exp⁡(− w T x i )] −1 .…”

Section: Regularized Adaptive Classification With Retrieval Of Simmentioning

confidence: 99%

“…The adaptive classifier for x is obtained by modifying the objective function (2) as

\begin{matrix} L_{Ada} (boldw) = {false\sum}_{i = 1}^{N} \log p (y_{i}^{}, {boldx}_{i}^{}; boldw) + {false\sum}_{j = 1}^{N_{r}} β_{j} \log p (y_{j}^{0.75em0.75em(r 0.75em0.75em)}, {boldx}_{j}^{0.75em0.75em(r 0.75em0.75em)}; boldw), \end{matrix}

where the weighting factors β j are defined according to the similarity of x j ( r ) to the query x [22]. These factors are larger than 1 in magnitude.…”

Section: Regularized Adaptive Classification With Retrieval Of Simmentioning

confidence: 99%

“…One drawback of the adaptive classifier in [22] is the extra cost associated with retraining of the classifier for each query case. It also needs access to the training cases of the baseline classifier, which are required for retraining the classifier.…”

Section: Introductionmentioning

confidence: 99%

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Regularization in Retrieval-Driven Classification of Clustered Microcalcifications for Breast Cancer

Jing

Yang

Nishikawa

2012

International Journal of Biomedical Imaging

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We propose a regularization based approach for case-adaptive classification in computer-aided diagnosis (CAD) of breast cancer. The goal is to improve the classification accuracy on a query case by making use of a set of similar cases retrieved from an existing library of known cases. In the proposed approach, a prior is first derived from a traditional CAD classifier (which is typically pre-trained offline on a set of training cases). It is then used together with the retrieved similar cases to obtain an adaptive classifier on the query case. We consider two different forms for the regularization prior: one is fixed for all query cases and the other is allowed to vary with different query cases. In the experiments the proposed approach is demonstrated on a dataset of 1,006 clinical cases. The results show that it could achieve significant improvement in numerical efficiency compared with a previously proposed case adaptive approach (by about an order of magnitude) while maintaining similar (or better) improvement in classification accuracy; it could also adapt faster in performance with a small number of retrieved cases. Measured by the area of under the ROC curve (AUC), the regularization based approach achieved AUC = 0.8215, compared with AUC = 0.7329 for the baseline classifier (P-value = 0.001).

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Section: Regularized Adaptive Classification With Retrieval Of Simmentioning

confidence: 99%

“…In [22], we considered logistic regression [26], in which w is determined by maximizing the following log-likelihood function:

\begin{matrix} L (boldw) = {false\sum}_{i = 1}^{N} \log p (y_{i}, {boldx}_{i}; boldw), \end{matrix}

where p ( y i = 1, x i ; w ) = [1+exp⁡(− w T x i )] −1 .…”

Section: Regularized Adaptive Classification With Retrieval Of Simmentioning

confidence: 99%

“…The adaptive classifier for x is obtained by modifying the objective function (2) as

\begin{matrix} L_{Ada} (boldw) = {false\sum}_{i = 1}^{N} \log p (y_{i}^{}, {boldx}_{i}^{}; boldw) + {false\sum}_{j = 1}^{N_{r}} β_{j} \log p (y_{j}^{0.75em0.75em(r 0.75em0.75em)}, {boldx}_{j}^{0.75em0.75em(r 0.75em0.75em)}; boldw), \end{matrix}

where the weighting factors β j are defined according to the similarity of x j ( r ) to the query x [22]. These factors are larger than 1 in magnitude.…”

Section: Regularized Adaptive Classification With Retrieval Of Simmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Regularization in Retrieval-Driven Classification of Clustered Microcalcifications for Breast Cancer

Jing

Yang

Nishikawa

2012

International Journal of Biomedical Imaging

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“…9,12,[23][24][25][26][27][28][29] These nine features can be categorized into three groups: (1) image features describing individual MCs (Feature Group 1), including the standard deviation of the image contrast values of the MCs, and the maximum and the standard deviation of the sizes of the MCs, 9,12,23,24 (2) spatial clustering features of the MCs (Feature Group 2), including the number of MCs in a cluster, the area of the cluster, and the compactness of cluster, 12,24,25 and (3) texture-based features (Feature Group 3), including the energy, contrast, and correlation derived from the gray-level cooccurrence matrices (GLCM). [26][27][28][29] For completeness, the detailed definitions of these features are given in Appendix.…”

Section: A2amentioning

confidence: 99%

Analysis of perceived similarity between pairs of microcalcification clusters in mammograms

et al. 2014

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Purpose: Content-based image retrieval aims to assist radiologists by presenting example images with known pathology that are visually similar to the case being evaluated. In this work, the authors investigate several fundamental issues underlying the similarity ratings between pairs of microcalcification (MC) lesions on mammograms as judged by radiologists: the degree of variability in the similarity ratings, the impact of this variability on agreement between readers in retrieval of similar lesions, and the factors contributing to the readers' similarity ratings. Methods:The authors conduct a reader study on a set of 1000 image pairs of MC lesions, in which a group of experienced breast radiologists rated the degree of similarity between each image pair. The image pairs are selected, from among possible pairings of 222 cases (110 malignant, 112 benign), based on quantitative image attributes (features) and the results of a preliminary reader study. Next, the authors apply analysis of variance (ANOVA) to quantify the level of variability in the readers' similarity ratings, and study how the variability in individual reader ratings affects consistency between readers. The authors also measure the extent to which readers agree on images which are most similar to a given query, for which the Dice coefficient is used. To investigate how the similarity ratings potentially relate to the attributes underlying the cases, the authors study the fraction of perceptually similar images that also share the same benign or malignant pathology as the query image; moreover, the authors apply multidimensional scaling (MDS) to embed the cases according to their mutual perceptual similarity in a two-dimensional plot, which allows the authors to examine the manner in which similar lesions relate to one another in terms of benign or malignant pathology and clustered MCs. Results:The ANOVA results show that the coefficient of determination in the reader similarity ratings is 0.59. The variability level in the similarity ratings is proved to be a limiting factor, leading to only moderate correlation between the readers in their readings. The Dice coefficient, measuring agreement between readers in retrieval of similar images, can vary from 0.45 to 0.64 with different levels of similarity for individual readers, but is higher for average ratings from a group of readers (from 0.59 to 0.78). More importantly, the fraction of retrieved cases that match the benign or malignant pathology of the query image was found to increase with the degree of similarity among the retrieved images, reaching average value as high as 0.69 for the radiologists (p-value <10 −4 compared to random guessing). Moreover, MDS embedding of all the cases shows that cases having the same pathology tend to cluster together, and that neighboring cases in the plot tend to be similar in their clustered MCs. Conclusions: While individual readers exhibit substantial variability in their similarity ratings, similarity ratings averaged from a group of readers can achieve a h...

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Computer-Aided Breast Cancer Diagnosis with Optimal Feature Sets: Reduction Rules and Optimization Techniques

Mathieson

Mendes

Marsden

et al. 2016

Methods in Molecular Biology

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This chapter introduces a new method for knowledge extraction from databases for the purpose of finding a discriminative set of features that is also a robust set for within-class classification. Our method is generic and we introduce it here in the field of breast cancer diagnosis from digital mammography data. The mathematical formalism is based on a generalization of the k-Feature Set problem called (α, β)-k-Feature Set problem, introduced by Cotta and Moscato (J Comput Syst Sci 67(4):686-690, 2003). This method proceeds in two steps: first, an optimal (α, β)-k-feature set of minimum cardinality is identified and then, a set of classification rules using these features is obtained. We obtain the (α, β)-k-feature set in two phases; first a series of extremely powerful reduction techniques, which do not lose the optimal solution, are employed; and second, a metaheuristic search to identify the remaining features to be considered or disregarded. Two algorithms were tested with a public domain digital mammography dataset composed of 71 malignant and 75 benign cases. Based on the results provided by the algorithms, we obtain classification rules that employ only a subset of these features.

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Retrieval boosted computer-aided diagnosis of clustered microcalcifications for breast cancer

Cited by 12 publications

References 57 publications

Regularization in Retrieval-Driven Classification of Clustered Microcalcifications for Breast Cancer

Regularization in Retrieval-Driven Classification of Clustered Microcalcifications for Breast Cancer

Analysis of perceived similarity between pairs of microcalcification clusters in mammograms

Computer-Aided Breast Cancer Diagnosis with Optimal Feature Sets: Reduction Rules and Optimization Techniques

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