The Experts below are selected from a list of 60555 Experts worldwide ranked by ideXlab platform
Chulmin Joo - One of the best experts on this subject based on the ideXlab platform.
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intraoperative margin assessment of human breast tissue in optical coherence tomography images using deep neural networks
Computerized Medical Imaging and Graphics, 2018Co-Authors: Amal Rannen Triki, Matthew B Blaschko, Yoon Mo Jung, Seungri Song, Hyun Ju Han, Seung Il Kim, Chulmin JooAbstract:Abstract Assessing the surgical margin during breast lumpectomy operations can avoid the need for additional surgery. Optical coherence tomography (OCT) is an imaging technique that has been proven to be efficient for this purpose. However, to avoid overloading the surgeon during the operation, automatic cancer detection at the surface of the removed tissue is needed. This work explores automated margin assessment on a sample of patient data collected at the Pathology Department, Severance Hospital (Seoul, South Korea). Some methods based on the spatial statistics of the images have been developed, but the obtained results are still far from human performance. In this work, we investigate the possibility to use deep neural networks (DNNs) for real time margin assessment, demonstrating performance significantly better than the reported literature and close to the level of a human expert. Since the goal is to detect the presence of cancer, a patch-based classification method is proposed, as it is sufficient for detection, and requires training data that is easier and cheaper to collect than for other approaches such as segmentation. For that purpose, we train a DNN architecture that was proved to be efficient for small images on patches extracted from images containing only cancer or only Normal tissue as determined by pathologists in a university hospital. As the number of available images in all such studies is by necessity small relative to other deep network applications such as ImageNet, a good regularization method is needed. In this work, we propose to use a recently introduced Function Norm regularization that attempts to directly control the Function complexity, in contrast to classical approaches such as weight decay and DropOut. As neither the code nor the data of previous results are publicly available, the obtained results are compared with reported results in the literature for a conservative comparison. Moreover, our method is applied to locally collected data on several data configurations. The reported results are the average over the different trials. The experimental results show that the use of DNNs yields significantly better results than other techniques when evaluated in terms of sensitivity, specificity, F1 score, G-mean and Matthews correlation coefficient. Function Norm regularization yielded higher and more robust results than competing regularization methods. We have demonstrated a system that shows high promise for (partially) automated margin assessment of human breast tissue, Equal error rate (EER) is reduced from approximately 12% (the lowest reported in the literature) to 5% – a 58% reduction. The method is computationally feasible for intraoperative application (less than 2 s per image) at the only cost of a longer offline training time.
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Intraoperative margin assessment of human breast tissue in optical coherence tomography images using deep neural networks
arXiv: Machine Learning, 2017Co-Authors: Amal Rannen Triki, Matthew B Blaschko, Yoon Mo Jung, Seungri Song, Hyun Ju Han, Seung Il Kim, Chulmin JooAbstract:Objective: In this work, we perform margin assessment of human breast tissue from optical coherence tomography (OCT) images using deep neural networks (DNNs). This work simulates an intraoperative setting for breast cancer lumpectomy. Methods: To train the DNNs, we use both the state-of-the-art methods (Weight Decay and DropOut) and a newly introduced regularization method based on Function Norms. Commonly used methods can fail when only a small database is available. The use of a Function Norm introduces a direct control over the complexity of the Function with the aim of diminishing the risk of overfitting. Results: As neither the code nor the data of previous results are publicly available, the obtained results are compared with reported results in the literature for a conservative comparison. Moreover, our method is applied to locally collected data on several data configurations. The reported results are the average over the different trials. Conclusion: The experimental results show that the use of DNNs yields significantly better results than other techniques when evaluated in terms of sensitivity, specificity, F1 score, G-mean and Matthews correlation coefficient. Function Norm regularization yielded higher and more robust results than competing methods. Significance: We have demonstrated a system that shows high promise for (partially) automated margin assessment of human breast tissue, Equal error rate (EER) is reduced from approximately 12\% (the lowest reported in the literature) to 5\%\,--\,a 58\% reduction. The method is computationally feasible for intraoperative application (less than 2 seconds per image).
Amal Rannen Triki - One of the best experts on this subject based on the ideXlab platform.
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intraoperative margin assessment of human breast tissue in optical coherence tomography images using deep neural networks
Computerized Medical Imaging and Graphics, 2018Co-Authors: Amal Rannen Triki, Matthew B Blaschko, Yoon Mo Jung, Seungri Song, Hyun Ju Han, Seung Il Kim, Chulmin JooAbstract:Abstract Assessing the surgical margin during breast lumpectomy operations can avoid the need for additional surgery. Optical coherence tomography (OCT) is an imaging technique that has been proven to be efficient for this purpose. However, to avoid overloading the surgeon during the operation, automatic cancer detection at the surface of the removed tissue is needed. This work explores automated margin assessment on a sample of patient data collected at the Pathology Department, Severance Hospital (Seoul, South Korea). Some methods based on the spatial statistics of the images have been developed, but the obtained results are still far from human performance. In this work, we investigate the possibility to use deep neural networks (DNNs) for real time margin assessment, demonstrating performance significantly better than the reported literature and close to the level of a human expert. Since the goal is to detect the presence of cancer, a patch-based classification method is proposed, as it is sufficient for detection, and requires training data that is easier and cheaper to collect than for other approaches such as segmentation. For that purpose, we train a DNN architecture that was proved to be efficient for small images on patches extracted from images containing only cancer or only Normal tissue as determined by pathologists in a university hospital. As the number of available images in all such studies is by necessity small relative to other deep network applications such as ImageNet, a good regularization method is needed. In this work, we propose to use a recently introduced Function Norm regularization that attempts to directly control the Function complexity, in contrast to classical approaches such as weight decay and DropOut. As neither the code nor the data of previous results are publicly available, the obtained results are compared with reported results in the literature for a conservative comparison. Moreover, our method is applied to locally collected data on several data configurations. The reported results are the average over the different trials. The experimental results show that the use of DNNs yields significantly better results than other techniques when evaluated in terms of sensitivity, specificity, F1 score, G-mean and Matthews correlation coefficient. Function Norm regularization yielded higher and more robust results than competing regularization methods. We have demonstrated a system that shows high promise for (partially) automated margin assessment of human breast tissue, Equal error rate (EER) is reduced from approximately 12% (the lowest reported in the literature) to 5% – a 58% reduction. The method is computationally feasible for intraoperative application (less than 2 s per image) at the only cost of a longer offline training time.
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stochastic weighted Function Norm regularization
2017Co-Authors: Amal Rannen Triki, Maxim Berman, Matthew B BlaschkoAbstract:Deep neural networks (DNNs) have become increasingly important due to their excellent empirical performance on a wide range of problems. However, regularization is generally achieved by indirect means, largely due to the complex set of Functions defined by a network and the difficulty in measuring Function complexity. There exists no method in the literature for additive regularization based on a Norm of the Function, as is classically considered in statistical learning theory. In this work, we propose sampling-based approximations to weighted Function Norms as regularizers for deep neural networks. We provide, to the best of our knowledge, the first proof in the literature of the NP-hardness of computing Function Norms of DNNs, motivating the necessity of a stochastic optimization strategy. Based on our proposed regularization scheme, stability-based bounds yield a $\mathcal{O}(N^{-\frac{1}{2}})$ generalization error for our proposed regularizer when applied to convex Function sets. We demonstrate broad conditions for the convergence of stochastic gradient descent on our objective, including for non-convex Function sets such as those defined by DNNs. Finally, we empirically validate the improved performance of the proposed regularization strategy for both convex Function sets as well as DNNs on real-world classification and segmentation tasks.
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Intraoperative margin assessment of human breast tissue in optical coherence tomography images using deep neural networks
arXiv: Machine Learning, 2017Co-Authors: Amal Rannen Triki, Matthew B Blaschko, Yoon Mo Jung, Seungri Song, Hyun Ju Han, Seung Il Kim, Chulmin JooAbstract:Objective: In this work, we perform margin assessment of human breast tissue from optical coherence tomography (OCT) images using deep neural networks (DNNs). This work simulates an intraoperative setting for breast cancer lumpectomy. Methods: To train the DNNs, we use both the state-of-the-art methods (Weight Decay and DropOut) and a newly introduced regularization method based on Function Norms. Commonly used methods can fail when only a small database is available. The use of a Function Norm introduces a direct control over the complexity of the Function with the aim of diminishing the risk of overfitting. Results: As neither the code nor the data of previous results are publicly available, the obtained results are compared with reported results in the literature for a conservative comparison. Moreover, our method is applied to locally collected data on several data configurations. The reported results are the average over the different trials. Conclusion: The experimental results show that the use of DNNs yields significantly better results than other techniques when evaluated in terms of sensitivity, specificity, F1 score, G-mean and Matthews correlation coefficient. Function Norm regularization yielded higher and more robust results than competing methods. Significance: We have demonstrated a system that shows high promise for (partially) automated margin assessment of human breast tissue, Equal error rate (EER) is reduced from approximately 12\% (the lowest reported in the literature) to 5\%\,--\,a 58\% reduction. The method is computationally feasible for intraoperative application (less than 2 seconds per image).
Matthew B Blaschko - One of the best experts on this subject based on the ideXlab platform.
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intraoperative margin assessment of human breast tissue in optical coherence tomography images using deep neural networks
Computerized Medical Imaging and Graphics, 2018Co-Authors: Amal Rannen Triki, Matthew B Blaschko, Yoon Mo Jung, Seungri Song, Hyun Ju Han, Seung Il Kim, Chulmin JooAbstract:Abstract Assessing the surgical margin during breast lumpectomy operations can avoid the need for additional surgery. Optical coherence tomography (OCT) is an imaging technique that has been proven to be efficient for this purpose. However, to avoid overloading the surgeon during the operation, automatic cancer detection at the surface of the removed tissue is needed. This work explores automated margin assessment on a sample of patient data collected at the Pathology Department, Severance Hospital (Seoul, South Korea). Some methods based on the spatial statistics of the images have been developed, but the obtained results are still far from human performance. In this work, we investigate the possibility to use deep neural networks (DNNs) for real time margin assessment, demonstrating performance significantly better than the reported literature and close to the level of a human expert. Since the goal is to detect the presence of cancer, a patch-based classification method is proposed, as it is sufficient for detection, and requires training data that is easier and cheaper to collect than for other approaches such as segmentation. For that purpose, we train a DNN architecture that was proved to be efficient for small images on patches extracted from images containing only cancer or only Normal tissue as determined by pathologists in a university hospital. As the number of available images in all such studies is by necessity small relative to other deep network applications such as ImageNet, a good regularization method is needed. In this work, we propose to use a recently introduced Function Norm regularization that attempts to directly control the Function complexity, in contrast to classical approaches such as weight decay and DropOut. As neither the code nor the data of previous results are publicly available, the obtained results are compared with reported results in the literature for a conservative comparison. Moreover, our method is applied to locally collected data on several data configurations. The reported results are the average over the different trials. The experimental results show that the use of DNNs yields significantly better results than other techniques when evaluated in terms of sensitivity, specificity, F1 score, G-mean and Matthews correlation coefficient. Function Norm regularization yielded higher and more robust results than competing regularization methods. We have demonstrated a system that shows high promise for (partially) automated margin assessment of human breast tissue, Equal error rate (EER) is reduced from approximately 12% (the lowest reported in the literature) to 5% – a 58% reduction. The method is computationally feasible for intraoperative application (less than 2 s per image) at the only cost of a longer offline training time.
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stochastic weighted Function Norm regularization
2017Co-Authors: Amal Rannen Triki, Maxim Berman, Matthew B BlaschkoAbstract:Deep neural networks (DNNs) have become increasingly important due to their excellent empirical performance on a wide range of problems. However, regularization is generally achieved by indirect means, largely due to the complex set of Functions defined by a network and the difficulty in measuring Function complexity. There exists no method in the literature for additive regularization based on a Norm of the Function, as is classically considered in statistical learning theory. In this work, we propose sampling-based approximations to weighted Function Norms as regularizers for deep neural networks. We provide, to the best of our knowledge, the first proof in the literature of the NP-hardness of computing Function Norms of DNNs, motivating the necessity of a stochastic optimization strategy. Based on our proposed regularization scheme, stability-based bounds yield a $\mathcal{O}(N^{-\frac{1}{2}})$ generalization error for our proposed regularizer when applied to convex Function sets. We demonstrate broad conditions for the convergence of stochastic gradient descent on our objective, including for non-convex Function sets such as those defined by DNNs. Finally, we empirically validate the improved performance of the proposed regularization strategy for both convex Function sets as well as DNNs on real-world classification and segmentation tasks.
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Intraoperative margin assessment of human breast tissue in optical coherence tomography images using deep neural networks
arXiv: Machine Learning, 2017Co-Authors: Amal Rannen Triki, Matthew B Blaschko, Yoon Mo Jung, Seungri Song, Hyun Ju Han, Seung Il Kim, Chulmin JooAbstract:Objective: In this work, we perform margin assessment of human breast tissue from optical coherence tomography (OCT) images using deep neural networks (DNNs). This work simulates an intraoperative setting for breast cancer lumpectomy. Methods: To train the DNNs, we use both the state-of-the-art methods (Weight Decay and DropOut) and a newly introduced regularization method based on Function Norms. Commonly used methods can fail when only a small database is available. The use of a Function Norm introduces a direct control over the complexity of the Function with the aim of diminishing the risk of overfitting. Results: As neither the code nor the data of previous results are publicly available, the obtained results are compared with reported results in the literature for a conservative comparison. Moreover, our method is applied to locally collected data on several data configurations. The reported results are the average over the different trials. Conclusion: The experimental results show that the use of DNNs yields significantly better results than other techniques when evaluated in terms of sensitivity, specificity, F1 score, G-mean and Matthews correlation coefficient. Function Norm regularization yielded higher and more robust results than competing methods. Significance: We have demonstrated a system that shows high promise for (partially) automated margin assessment of human breast tissue, Equal error rate (EER) is reduced from approximately 12\% (the lowest reported in the literature) to 5\%\,--\,a 58\% reduction. The method is computationally feasible for intraoperative application (less than 2 seconds per image).
Seungri Song - One of the best experts on this subject based on the ideXlab platform.
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intraoperative margin assessment of human breast tissue in optical coherence tomography images using deep neural networks
Computerized Medical Imaging and Graphics, 2018Co-Authors: Amal Rannen Triki, Matthew B Blaschko, Yoon Mo Jung, Seungri Song, Hyun Ju Han, Seung Il Kim, Chulmin JooAbstract:Abstract Assessing the surgical margin during breast lumpectomy operations can avoid the need for additional surgery. Optical coherence tomography (OCT) is an imaging technique that has been proven to be efficient for this purpose. However, to avoid overloading the surgeon during the operation, automatic cancer detection at the surface of the removed tissue is needed. This work explores automated margin assessment on a sample of patient data collected at the Pathology Department, Severance Hospital (Seoul, South Korea). Some methods based on the spatial statistics of the images have been developed, but the obtained results are still far from human performance. In this work, we investigate the possibility to use deep neural networks (DNNs) for real time margin assessment, demonstrating performance significantly better than the reported literature and close to the level of a human expert. Since the goal is to detect the presence of cancer, a patch-based classification method is proposed, as it is sufficient for detection, and requires training data that is easier and cheaper to collect than for other approaches such as segmentation. For that purpose, we train a DNN architecture that was proved to be efficient for small images on patches extracted from images containing only cancer or only Normal tissue as determined by pathologists in a university hospital. As the number of available images in all such studies is by necessity small relative to other deep network applications such as ImageNet, a good regularization method is needed. In this work, we propose to use a recently introduced Function Norm regularization that attempts to directly control the Function complexity, in contrast to classical approaches such as weight decay and DropOut. As neither the code nor the data of previous results are publicly available, the obtained results are compared with reported results in the literature for a conservative comparison. Moreover, our method is applied to locally collected data on several data configurations. The reported results are the average over the different trials. The experimental results show that the use of DNNs yields significantly better results than other techniques when evaluated in terms of sensitivity, specificity, F1 score, G-mean and Matthews correlation coefficient. Function Norm regularization yielded higher and more robust results than competing regularization methods. We have demonstrated a system that shows high promise for (partially) automated margin assessment of human breast tissue, Equal error rate (EER) is reduced from approximately 12% (the lowest reported in the literature) to 5% – a 58% reduction. The method is computationally feasible for intraoperative application (less than 2 s per image) at the only cost of a longer offline training time.
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Intraoperative margin assessment of human breast tissue in optical coherence tomography images using deep neural networks
arXiv: Machine Learning, 2017Co-Authors: Amal Rannen Triki, Matthew B Blaschko, Yoon Mo Jung, Seungri Song, Hyun Ju Han, Seung Il Kim, Chulmin JooAbstract:Objective: In this work, we perform margin assessment of human breast tissue from optical coherence tomography (OCT) images using deep neural networks (DNNs). This work simulates an intraoperative setting for breast cancer lumpectomy. Methods: To train the DNNs, we use both the state-of-the-art methods (Weight Decay and DropOut) and a newly introduced regularization method based on Function Norms. Commonly used methods can fail when only a small database is available. The use of a Function Norm introduces a direct control over the complexity of the Function with the aim of diminishing the risk of overfitting. Results: As neither the code nor the data of previous results are publicly available, the obtained results are compared with reported results in the literature for a conservative comparison. Moreover, our method is applied to locally collected data on several data configurations. The reported results are the average over the different trials. Conclusion: The experimental results show that the use of DNNs yields significantly better results than other techniques when evaluated in terms of sensitivity, specificity, F1 score, G-mean and Matthews correlation coefficient. Function Norm regularization yielded higher and more robust results than competing methods. Significance: We have demonstrated a system that shows high promise for (partially) automated margin assessment of human breast tissue, Equal error rate (EER) is reduced from approximately 12\% (the lowest reported in the literature) to 5\%\,--\,a 58\% reduction. The method is computationally feasible for intraoperative application (less than 2 seconds per image).
Hyun Ju Han - One of the best experts on this subject based on the ideXlab platform.
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intraoperative margin assessment of human breast tissue in optical coherence tomography images using deep neural networks
Computerized Medical Imaging and Graphics, 2018Co-Authors: Amal Rannen Triki, Matthew B Blaschko, Yoon Mo Jung, Seungri Song, Hyun Ju Han, Seung Il Kim, Chulmin JooAbstract:Abstract Assessing the surgical margin during breast lumpectomy operations can avoid the need for additional surgery. Optical coherence tomography (OCT) is an imaging technique that has been proven to be efficient for this purpose. However, to avoid overloading the surgeon during the operation, automatic cancer detection at the surface of the removed tissue is needed. This work explores automated margin assessment on a sample of patient data collected at the Pathology Department, Severance Hospital (Seoul, South Korea). Some methods based on the spatial statistics of the images have been developed, but the obtained results are still far from human performance. In this work, we investigate the possibility to use deep neural networks (DNNs) for real time margin assessment, demonstrating performance significantly better than the reported literature and close to the level of a human expert. Since the goal is to detect the presence of cancer, a patch-based classification method is proposed, as it is sufficient for detection, and requires training data that is easier and cheaper to collect than for other approaches such as segmentation. For that purpose, we train a DNN architecture that was proved to be efficient for small images on patches extracted from images containing only cancer or only Normal tissue as determined by pathologists in a university hospital. As the number of available images in all such studies is by necessity small relative to other deep network applications such as ImageNet, a good regularization method is needed. In this work, we propose to use a recently introduced Function Norm regularization that attempts to directly control the Function complexity, in contrast to classical approaches such as weight decay and DropOut. As neither the code nor the data of previous results are publicly available, the obtained results are compared with reported results in the literature for a conservative comparison. Moreover, our method is applied to locally collected data on several data configurations. The reported results are the average over the different trials. The experimental results show that the use of DNNs yields significantly better results than other techniques when evaluated in terms of sensitivity, specificity, F1 score, G-mean and Matthews correlation coefficient. Function Norm regularization yielded higher and more robust results than competing regularization methods. We have demonstrated a system that shows high promise for (partially) automated margin assessment of human breast tissue, Equal error rate (EER) is reduced from approximately 12% (the lowest reported in the literature) to 5% – a 58% reduction. The method is computationally feasible for intraoperative application (less than 2 s per image) at the only cost of a longer offline training time.
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Intraoperative margin assessment of human breast tissue in optical coherence tomography images using deep neural networks
arXiv: Machine Learning, 2017Co-Authors: Amal Rannen Triki, Matthew B Blaschko, Yoon Mo Jung, Seungri Song, Hyun Ju Han, Seung Il Kim, Chulmin JooAbstract:Objective: In this work, we perform margin assessment of human breast tissue from optical coherence tomography (OCT) images using deep neural networks (DNNs). This work simulates an intraoperative setting for breast cancer lumpectomy. Methods: To train the DNNs, we use both the state-of-the-art methods (Weight Decay and DropOut) and a newly introduced regularization method based on Function Norms. Commonly used methods can fail when only a small database is available. The use of a Function Norm introduces a direct control over the complexity of the Function with the aim of diminishing the risk of overfitting. Results: As neither the code nor the data of previous results are publicly available, the obtained results are compared with reported results in the literature for a conservative comparison. Moreover, our method is applied to locally collected data on several data configurations. The reported results are the average over the different trials. Conclusion: The experimental results show that the use of DNNs yields significantly better results than other techniques when evaluated in terms of sensitivity, specificity, F1 score, G-mean and Matthews correlation coefficient. Function Norm regularization yielded higher and more robust results than competing methods. Significance: We have demonstrated a system that shows high promise for (partially) automated margin assessment of human breast tissue, Equal error rate (EER) is reduced from approximately 12\% (the lowest reported in the literature) to 5\%\,--\,a 58\% reduction. The method is computationally feasible for intraoperative application (less than 2 seconds per image).
