The Experts below are selected from a list of 20478 Experts worldwide ranked by ideXlab platform
Lingquan Kong - One of the best experts on this subject based on the ideXlab platform.
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Advances in Ultrasound-Guided Vacuum-Assisted Biopsy of Breast Microcalcifications.
Ultrasound in medicine & biology, 2021Co-Authors: Rong Kong, Shen Tian, Jia-shuo Liu, Bao-shan Zou, Lingquan KongAbstract:Microcalcification is one of the significant indications for or can even be the sole mammographic feature of breast cancer, especially occult breast cancer. Biopsy and pathologic examination are the most important methods used to identify the nature of suspicious Microcalcifications. Stereotactic vacuum-assisted breast biopsy (S-VAB) is the most commonly used biopsy method for Microcalcifications currently because of the high detection rate of mammography for Microcalcifications. However, in recent years, several clinical studies have gradually found that ultrasound-guided vacuum-assisted breast biopsy (US-VAB) could be an alternative to S-VAB for Microcalcifications to some extent, and has its own advantages of flexibility, real-time performance, comfort and high accessibility compared with mammography. An overview of US-VAB of Microcalcifications is provided with respect to success rate, diagnostic accuracy, advantages and limitations. On the basis of numerous studies and clinical experience, US-VAB proved to be a valid alternative to S-VAB, with comparable diagnostic accuracy if the microcalcification foci could be detected by ultrasound. And for patients with ultrasound-invisible Microcalcifications who are not suitable for or tolerable of S-VAB, US-VAB combined with mammography localization of Microcalcifications can also be considered.
Mark A Helvie - One of the best experts on this subject based on the ideXlab platform.
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deep learning convolution neural network for computer aided detection of Microcalcifications in digital breast tomosynthesis
Proceedings of SPIE, 2016Co-Authors: Ravi K Samala, Heang Ping Chan, Lubomir M Hadjiiski, Kenny H Cha, Mark A HelvieAbstract:A deep learning convolution neural network (DLCNN) was designed to differentiate microcalcification candidates detected during the prescreening stage as true calcifications or false positives in a computer-aided detection (CAD) system for clustered Microcalcifications. The microcalcification candidates were extracted from the planar projection image generated from the digital breast tomosynthesis volume reconstructed by a multiscale bilateral filtering regularized simultaneous algebraic reconstruction technique. For training and testing of the DLCNN, true Microcalcifications are manually labeled for the data sets and false positives were obtained from the candidate objects identified by the CAD system at prescreening after exclusion of the true Microcalcifications. The DLCNN architecture was selected by varying the number of filters, filter kernel sizes and gradient computation parameter in the convolution layers, resulting in a parameter space of 216 combinations. The exhaustive grid search method was used to select an optimal architecture within the parameter space studied, guided by the area under the receiver operating characteristic curve (AUC) as a figure-of-merit. The effects of varying different categories of the parameter space were analyzed. The selected DLCNN was compared with our previously designed CNN architecture for the test set. The AUCs of the CNN and DLCNN was 0.89 and 0.93, respectively. The improvement was statistically significant (p < 0.05).
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digital breast tomosynthesis computer aided detection of clustered Microcalcifications on planar projection images
Physics in Medicine and Biology, 2014Co-Authors: Ravi K Samala, Heang Ping Chan, Lubomir M Hadjiiski, Jun Wei, Mark A HelvieAbstract:This paper describes a new approach to detect microcalcification clusters (MCs) in digital breast tomosynthesis (DBT) via its planar projection (PPJ) image. With IRB approval, two-view (cranio-caudal and mediolateral oblique views) DBTs of human subject breasts were obtained with a GE GEN2 prototype DBT system that acquires 21 projection angles spanning 60° in 3° increments. A data set of 307 volumes (154 human subjects) was divided by case into independent training (127 with MCs) and test sets (104 with MCs and 76 free of MCs). A simultaneous algebraic reconstruction technique with multiscale bilateral filtering (MSBF) regularization was used to enhance Microcalcifications and suppress noise. During the MSBF regularized reconstruction, the DBT volume was separated into high frequency (HF) and low frequency components representing Microcalcifications and larger structures. At the final iteration, maximum intensity projection was applied to the regularized HF volume to generate a PPJ image that contained MCs with increased contrast-to-noise ratio (CNR) and reduced search space. High CNR objects in the PPJ image were extracted and labeled as microcalcification candidates. Convolution neural network trained to recognize the image pattern of Microcalcifications was used to classify the candidates into true calcifications and tissue structures and artifacts. The remaining microcalcification candidates were grouped into MCs by dynamic conditional clustering based on adaptive CNR threshold and radial distance criteria. False positive (FP) clusters were further reduced using the number of candidates in a cluster, CNR and size of microcalcification candidates. At 85% sensitivity an FP rate of 0.71 and 0.54 was achieved for view- and case-based sensitivity, respectively, compared to 2.16 and 0.85 achieved in DBT. The improvement was significant (p-value = 0.003) by JAFROC analysis.
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computer aided detection of clustered Microcalcifications in digital breast tomosynthesis a 3d approach
Medical Physics, 2011Co-Authors: Berkman Sahiner, Heang Ping Chan, Lubomir M Hadjiiski, Mark A Helvie, Jun Wei, Chuan ZhouAbstract:Purpose: To design a computer-aided detection (CADe) system for clustered Microcalcifications in reconstructed digital breast tomosynthesis (DBT) volumes and to perform a preliminary evaluation of the CADe system. Methods: IRB approval and informed consent were obtained in this study. A data set of two-view DBT of 72 breasts containing microcalcification clusters was collected from 72 subjects who were scheduled to undergo breast biopsy. Based on tissue sampling results, 17 cases had breast cancer and 55 were benign. A separate data set of two-view DBT of 38 breasts free of clustered Microcalcifications from 38 subjects was collected to independently estimate the number of false-positives (FPs) generated by the CADe system. A radiologist experienced in breast imaging marked the biopsied cluster of Microcalcifications with a 3D bounding box using all available clinical and imaging information. A CADe system was designed to detect microcalcification clusters in the reconstructed volume. The system consisted of prescreening, clustering, and false-positive reduction stages. In the prescreening stage, the conspicuity of microcalcification-like objects was increased by an enhancement-modulated 3D calcification response function. An iterative thresholding and 3D object growing method was used to detect cluster seed objects, which were used as potential centers of microcalcification clusters. In the cluster detection stage, microcalcification candidates were identified using a second iterative thresholding procedure, which was applied to the signal-to-noise ratio (SNR) enhanced image voxels with a positive calcification response. Starting with each cluster seed object as the initial cluster center, a dynamic clustering algorithm formed a cluster candidate by including microcalcification candidates within a 3D neighborhood of the cluster seed object that satisfied the clustering criteria. The number, size, and SNR of the Microcalcifications in a cluster candidate and the cluster shape were used to reduce the number of FPs. Results: The prescreening stage detected a cluster seed object in 94% of the biopsied microcalcification clusters at a threshold of 100 cluster seed objects per DBT volume. After clustering, the detection sensitivity was 90% at 15 marks per DBT volume. After FP reduction, at 85% sensitivity, the average number of FPs estimated using the data set containing microcalcification clusters was 3.8 per DBT volume, and that estimated using the data set free of microcalcification clusters was 3.4. The detection performance for malignant microcalcification clusters was superior to that for benign clusters. Conclusions: Our study indicates the feasibility of the 3D approach to the detection of clustered Microcalcifications in DBT and that the newly designed enhancement-modulated 3D calcification response function is promising for prescreening. Further work is needed to assess the generalizability of our approach and to improve its performance.
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computer aided detection of clusters of Microcalcifications on full field digital mammograms
Medical Physics, 2006Co-Authors: Berkman Sahiner, Heang Ping Chan, Lubomir M Hadjiiski, Mark A Helvie, Jun Wei, Chuan ZhouAbstract:We are developing a computer-aided detection (CAD) system to identify microcalcification clusters (MCCs) automatically on full field digital mammograms (FFDMs). The CAD system includes six stages: preprocessing; image enhancement; segmentation of microcalcification candidates; false positive (FP) reduction for individual Microcalcifications; regional clustering; and FP reduction for clustered Microcalcifications. At the stage of FP reduction for individual Microcalcifications, a truncated sum-of-squares error function was used to improve the efficiency and robustness of the training of an artificial neural network in our CAD system for FFDMs. At the stage of FP reduction for clustered Microcalcifications, morphological features and features derived from the artificial neural network outputs were extracted from each cluster. Stepwise linear discriminant analysis (LDA) was used to select the features. An LDA classifier was then used to differentiate clustered Microcalcifications from FPs. A data set of 96 cases with 192 images was collected at the University of Michigan. This data set contained 96 MCCs, of which 28 clusters were proven by biopsy to be malignant and 68 were proven to be benign. The data set was separated into two independent data sets for training and testing of the CAD system in a cross-validation scheme. When one data set was usedmore » to train and validate the convolution neural network (CNN) in our CAD system, the other data set was used to evaluate the detection performance. With the use of a truncated error metric, the training of CNN could be accelerated and the classification performance was improved. The CNN in combination with an LDA classifier could substantially reduce FPs with a small tradeoff in sensitivity. By using the free-response receiver operating characteristic methodology, it was found that our CAD system can achieve a cluster-based sensitivity of 70, 80, and 90 % at 0.21, 0.61, and 1.49 FPs/image, respectively. For case-based performance evaluation, a sensitivity of 70, 80, and 90 % can be achieved at 0.07, 0.17, and 0.65 FPs/image, respectively. We also used a data set of 216 mammograms negative for clustered Microcalcifications to further estimate the FP rate of our CAD system. The corresponding FP rates were 0.15, 0.31, and 0.86 FPs/image for cluster-based detection when negative mammograms were used for estimation of FP rates.« less
Reyer Zwiggelaar - One of the best experts on this subject based on the ideXlab platform.
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Multiscale connected chain topological modelling for microcalcification classification
Computers in biology and medicine, 2019Co-Authors: Minu George, Zhili Chen, Reyer ZwiggelaarAbstract:Abstract Computer-aided diagnosis (CAD) systems can be employed to help classify mammographic microcalcification clusters. In this paper, a novel method for the classification of the microcalcification clusters based on topology/connectivity has been introduced. The proposed method is distinct from existing techniques which concentrate on morphology and texture of Microcalcifications and surrounding tissue. The proposed approach used multiscale morphological relationship of connectivity between Microcalcifications where connected chains between nearest Microcalcifications were generated at each scale. Subsequently, graph connectivity features at each scale were extracted to estimate the topological connectivity structure of microcalcification clusters for benign versus malignant classification. The proposed approach was evaluated using publicly available digitized datasets: MIAS and DDSM, in addition to the digital OPTIMAM dataset. The classification of features using KNN obtained a classification accuracy of 86.47 ± 1.30 % , 90.0 ± 0.00 % , 82.5 ± 2.63 % , 76.75 ± 0.66 % for the DDSM, MIAS-manual, MIAS-auto and OPTIMAM datasets respectively. The study showed that topological/connectivity modelling using a multiscale approach was appropriate for microcalcification cluster analysis and classification; topological connectivity and distribution can be linked to clinical understanding of microcalcification spatial distribution.
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topological modeling and classification of mammographic microcalcification clusters
IEEE Transactions on Biomedical Engineering, 2015Co-Authors: Zhili Chen, Caroline Boggis, Arnau Oliver, Harry Strange, Erika R E Denton, Reyer ZwiggelaarAbstract:Goal: The presence of microcalcification clusters is a primary sign of breast cancer; however, it is difficult and time consuming for radiologists to classify Microcalcifications as malignant or benign. In this paper, a novel method for the classification of microcalcification clusters in mammograms is proposed. Methods: The topology/connectivity of individual Microcalcifications is analyzed within a cluster using multiscale morphology. This is distinct from existing approaches that tend to concentrate on the morphology of individual Microcalcifications and/or global (statistical) cluster features. A set of microcalcification graphs are generated to represent the topological structure of microcalcification clusters at different scales. Subsequently, graph theoretical features are extracted, which constitute the topological feature space for modeling and classifying microcalcification clusters. $k$ -nearest-neighbors-based classifiers are employed for classifying microcalcification clusters. Results: The validity of the proposed method is evaluated using two well-known digitized datasets (MIAS and DDSM) and a full-field digital dataset. High classification accuracies (up to 96%) and good ROC results (area under the ROC curve up to 0.96) are achieved. A full comparison with related publications is provided, which includes a direct comparison. Conclusion: The results indicate that the proposed approach is able to outperform the current state-of-the-art methods. Significance: This study shows that topology modeling is an important tool for microcalcification analysis not only because of the improved classification accuracy but also because the topological measures can be linked to clinical understanding.
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modelling mammographic microcalcification clusters using persistent mereotopology
Pattern Recognition Letters, 2014Co-Authors: Harry Strange, Zhili Chen, Erika R E Denton, Reyer ZwiggelaarAbstract:In mammographic imaging, the presence of Microcalcifications, small deposits of calcium in the breast, is a primary indicator of breast cancer. However, not all Microcalcifications are malignant and their distribution within the breast can be used to indicate whether clusters of Microcalcifications are benign or malignant. Computer-aided diagnosis (CAD) systems can be employed to help classify such microcalcification clusters. In this paper a novel method for classifying microcalcification clusters is presented by representing discrete mereotopological relations between the individual Microcalcifications over a range of scales in the form of a mereotopological barcode. This barcode based representation is able to model complex relations between multiple regions and the results on mammographic microcalcification data shows the effectiveness of this approach. Classification accuracies of 95% and 80% are achieved on the MIAS and DDSM datasets, respectively. These results are comparable to existing state-of-the art methods. This work also demonstrates that mereotopological barcodes could be used to help trained clinicians in their diagnosis by providing a clinical interpretation of barcodes that represent both benign and malignant cases.
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automatic microcalcification and cluster detection for digital and digitised mammograms
Knowledge Based Systems, 2012Co-Authors: Arnau Oliver, Meritxell Tortajada, Jordi Freixenet, Lidia Tortajada, Melcior Sentis, Xavier Lladó, Albert Torrent, Reyer ZwiggelaarAbstract:In this paper we present a knowledge-based approach for the automatic detection of Microcalcifications and clusters in mammographic images. Our proposal is based on using local features extracted from a bank of filters to obtain a local description of the Microcalcifications morphology. The developed approach performs an initial training step in order to automatically learn and select the most salient features, which are subsequently used in a boosted classifier to perform the detection of individual Microcalcifications. Subsequently, the microcalcification detection method is extended in order to detect clusters. The validity of our approach is extensively demonstrated using two digitised databases and one full-field digital database. The experimental evaluation is performed in terms of ROC analysis for the microcalcification detection and FROC analysis for the cluster detection, resulting in better than 80% sensitivity at 1 false positive cluster per image.
Woo Kyung Moon - One of the best experts on this subject based on the ideXlab platform.
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Automatic detection of Microcalcifications in breast ultrasound.
Medical Physics, 2013Co-Authors: Ruey-feng Chang, Jung Min Chang, Jeon-hor Chen, Chiun-sheng Huang, Woo Kyung MoonAbstract:Purpose: In an ultrasound (US) image, the presence of Microcalcifications within breast lesions is an important indicator of malignancy. The purpose of this study was to develop a novel automatic detection system to find Microcalcifications inside a breast lesion using an US image. Methods: Breast US images from 103 cases with Microcalcifications were obtained using an US system with a 6–14 MHz transducer, and 585 microcalcification foci marked on 103 breast US images by a radiologist were used as the ground truth. After segmentation of the lesion contour using the level set method, the microcalcification candidates inside the lesion were found using adaptive speckle reduction and top hat filters. Then, three criteria were used to identify the real Microcalcifications, including the mean, single point, and brightness criteria. Results: The proposed method revealed Microcalcifications within the lesions in all 103 cases. The sensitivity and the false positive (FP) rate for the detection of microcalcification foci were 80.3% (470/585) and 3.1 per case, respectively. The sensitivities and FP rates for the benign and malignant cases were 79.2% (243/307) with a FP rate of 3.5 and 81.7% (227/278) with a FP rate of 2.6, respectively. Conclusions: The authors’ proposed method has the potential to provide a tool to help physicians detect Microcalcifications within breast lesions.
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Microcalcification Detection in 3-D Breast Ultrasound
2005 IEEE Engineering in Medicine and Biology 27th Annual Conference, 2005Co-Authors: Ruey-feng Chang, Sheng-fang Huang, Dar Ren Chen, L. P. Wang, Woo Kyung MoonAbstract:The appearance of cluster of Microcalcifications in mammography or sonography is an important indicator for malignancy. Microcalcifications are calcium deposits, which can be identified as tiny areas that are slightly brighter than surrounding tissue. Detection of mammographic microcalcification has been proposed in many studies. Since a microcalcification cluster is a three-dimensional (3-D) entity, its projection onto a two-dimensional (2-D) image results in a loss of spatial information and may also cause superimposition of individual calcifications within the cluster. This paper aims to use the 3-D ultrasound to determine Microcalcifications. In each slice, the proposed method adopts the top-hat filter to find bright spots, and employs four 2-D criteria to select the spots as candidate Microcalcifications. Finally, spots appearing in sequent slices at the same position are considered as a microcalcification. We suggest using a computer automatically to detect the microcalcification being feasible and Microcalcifications being a very important criterion of malignancy on future developing the computer-aided diagnosis for ultrasound. In the future, this technique can be adopted in a computer-aided diagnosis system combined with other diagnosis features for improving the diagnosis performance
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US of Mammographically Detected Clustered Microcalcifications
Radiology, 2000Co-Authors: Woo Kyung Moon, Jung Gi Im, In Ae ParkAbstract:PURPOSE: To determine whether ultrasonography (US) can depict breast masses associated with mammographically detected clustered Microcalcifications and whether the visibility at US is different between benign and malignant lesions. MATERIALS AND METHODS: Ninety-four patients with 100 mammographically detected microcalcification clusters prospectively underwent US with a 10- or 12-MHz transducer before mammographically guided presurgical hook-wire localization. The visibility of breast masses at US was correlated with histologic and mammographic findings. RESULTS: Surgical biopsy revealed 62 benign lesions, 30 intraductal cancers, and eight invasive cancers. At US, breast masses associated with Microcalcifications were seen in 45 (45%) of 100 cases. US depicted more breast masses associated with malignant (31 [82%] of 38) than with benign (14 [23%] of 62) Microcalcifications (P
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us of mammographically detected clustered Microcalcifications
Radiology, 2000Co-Authors: Woo Kyung Moon, Young Hwan Koh, Dongyoung Noh, In Ae ParkAbstract:PURPOSE: To determine whether ultrasonography (US) can depict breast masses associated with mammographically detected clustered Microcalcifications and whether the visibility at US is different between benign and malignant lesions. MATERIALS AND METHODS: Ninety-four patients with 100 mammographically detected microcalcification clusters prospectively underwent US with a 10- or 12-MHz transducer before mammographically guided presurgical hook-wire localization. The visibility of breast masses at US was correlated with histologic and mammographic findings. RESULTS: Surgical biopsy revealed 62 benign lesions, 30 intraductal cancers, and eight invasive cancers. At US, breast masses associated with Microcalcifications were seen in 45 (45%) of 100 cases. US depicted more breast masses associated with malignant (31 [82%] of 38) than with benign (14 [23%] of 62) Microcalcifications (P < .001). In malignant microcalcification clusters larger than 10 mm, US depicted associated breast masses in all 25 cases. There w...
Chuan Zhou - One of the best experts on this subject based on the ideXlab platform.
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multiscale regularized reconstruction for enhancing microcalcification in digital breast tomosynthesis
Proceedings of SPIE, 2012Co-Authors: Yao Lu, Heang Ping Chan, Lubomir M Hadjiiski, Chuan ZhouAbstract:Digital breast tomosynthesis (DBT) holds strong promise for improving the sensitivity of detecting subtle mass lesions. Detection of Microcalcifications is more difficult because of high noise and subtle signals in the large DBT volume. It is important to enhance the contrast-to-noise ratio (CNR) of Microcalcifications in DBT reconstruction. A major challenge of implementing microcalcification enhancement or noise regularization in DBT reconstruction is to preserve the image quality of masses, especially those with ill-defined margins and subtle spiculations. We are developing a new multiscale regularization (MSR) method for the simultaneous algebraic reconstruction technique (SART) to improve the CNR of Microcalcifications without compromising the quality of masses. Each DBT slice is stratified into different frequency bands via wavelet decomposition and the regularization method applies different degrees of regularization to different frequency bands to preserve features of interest and suppress noise. Regularization is constrained by a characteristic map to avoid smoothing subtle Microcalcifications. The characteristic map is generated via image feature analysis to identify potential microcalcification locations in the DBT volume. The MSR method was compared to the non-convex total pvariation (TpV) method and SART with no regularization (NR) in terms of the CNR and the full width at half maximum of the line profiles intersecting calcifications and mass spiculations in DBT of human subjects. The results demonstrated that SART regularized by the MSR method was superior to the TpV method for subtle Microcalcifications in terms of CNR enhancement. The MSR method preserved the quality of subtle spiculations better than the TpV method in comparison to NR.
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computer aided detection of clustered Microcalcifications in digital breast tomosynthesis a 3d approach
Medical Physics, 2011Co-Authors: Berkman Sahiner, Heang Ping Chan, Lubomir M Hadjiiski, Mark A Helvie, Jun Wei, Chuan ZhouAbstract:Purpose: To design a computer-aided detection (CADe) system for clustered Microcalcifications in reconstructed digital breast tomosynthesis (DBT) volumes and to perform a preliminary evaluation of the CADe system. Methods: IRB approval and informed consent were obtained in this study. A data set of two-view DBT of 72 breasts containing microcalcification clusters was collected from 72 subjects who were scheduled to undergo breast biopsy. Based on tissue sampling results, 17 cases had breast cancer and 55 were benign. A separate data set of two-view DBT of 38 breasts free of clustered Microcalcifications from 38 subjects was collected to independently estimate the number of false-positives (FPs) generated by the CADe system. A radiologist experienced in breast imaging marked the biopsied cluster of Microcalcifications with a 3D bounding box using all available clinical and imaging information. A CADe system was designed to detect microcalcification clusters in the reconstructed volume. The system consisted of prescreening, clustering, and false-positive reduction stages. In the prescreening stage, the conspicuity of microcalcification-like objects was increased by an enhancement-modulated 3D calcification response function. An iterative thresholding and 3D object growing method was used to detect cluster seed objects, which were used as potential centers of microcalcification clusters. In the cluster detection stage, microcalcification candidates were identified using a second iterative thresholding procedure, which was applied to the signal-to-noise ratio (SNR) enhanced image voxels with a positive calcification response. Starting with each cluster seed object as the initial cluster center, a dynamic clustering algorithm formed a cluster candidate by including microcalcification candidates within a 3D neighborhood of the cluster seed object that satisfied the clustering criteria. The number, size, and SNR of the Microcalcifications in a cluster candidate and the cluster shape were used to reduce the number of FPs. Results: The prescreening stage detected a cluster seed object in 94% of the biopsied microcalcification clusters at a threshold of 100 cluster seed objects per DBT volume. After clustering, the detection sensitivity was 90% at 15 marks per DBT volume. After FP reduction, at 85% sensitivity, the average number of FPs estimated using the data set containing microcalcification clusters was 3.8 per DBT volume, and that estimated using the data set free of microcalcification clusters was 3.4. The detection performance for malignant microcalcification clusters was superior to that for benign clusters. Conclusions: Our study indicates the feasibility of the 3D approach to the detection of clustered Microcalcifications in DBT and that the newly designed enhancement-modulated 3D calcification response function is promising for prescreening. Further work is needed to assess the generalizability of our approach and to improve its performance.
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computer aided detection of clusters of Microcalcifications on full field digital mammograms
Medical Physics, 2006Co-Authors: Berkman Sahiner, Heang Ping Chan, Lubomir M Hadjiiski, Mark A Helvie, Jun Wei, Chuan ZhouAbstract:We are developing a computer-aided detection (CAD) system to identify microcalcification clusters (MCCs) automatically on full field digital mammograms (FFDMs). The CAD system includes six stages: preprocessing; image enhancement; segmentation of microcalcification candidates; false positive (FP) reduction for individual Microcalcifications; regional clustering; and FP reduction for clustered Microcalcifications. At the stage of FP reduction for individual Microcalcifications, a truncated sum-of-squares error function was used to improve the efficiency and robustness of the training of an artificial neural network in our CAD system for FFDMs. At the stage of FP reduction for clustered Microcalcifications, morphological features and features derived from the artificial neural network outputs were extracted from each cluster. Stepwise linear discriminant analysis (LDA) was used to select the features. An LDA classifier was then used to differentiate clustered Microcalcifications from FPs. A data set of 96 cases with 192 images was collected at the University of Michigan. This data set contained 96 MCCs, of which 28 clusters were proven by biopsy to be malignant and 68 were proven to be benign. The data set was separated into two independent data sets for training and testing of the CAD system in a cross-validation scheme. When one data set was usedmore » to train and validate the convolution neural network (CNN) in our CAD system, the other data set was used to evaluate the detection performance. With the use of a truncated error metric, the training of CNN could be accelerated and the classification performance was improved. The CNN in combination with an LDA classifier could substantially reduce FPs with a small tradeoff in sensitivity. By using the free-response receiver operating characteristic methodology, it was found that our CAD system can achieve a cluster-based sensitivity of 70, 80, and 90 % at 0.21, 0.61, and 1.49 FPs/image, respectively. For case-based performance evaluation, a sensitivity of 70, 80, and 90 % can be achieved at 0.07, 0.17, and 0.65 FPs/image, respectively. We also used a data set of 216 mammograms negative for clustered Microcalcifications to further estimate the FP rate of our CAD system. The corresponding FP rates were 0.15, 0.31, and 0.86 FPs/image for cluster-based detection when negative mammograms were used for estimation of FP rates.« less