Browsing by Author "Gray, Kathryn"

Now showing 1 - 4 of 4
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    Coupled IGMM-GANs for improved generative adversarial anomaly detection
    (IEEE, 2018-12) Gray, Kathryn; Smolyak, Daniel; Badirli, Sarkhan; Mohler, George; Computer and Information Science, School of Science
    Detecting anomalies and outliers in data has a number of applications including hazard sensing, fraud detection, and systems management. While generative adversarial networks seem like a natural fit for addressing these challenges, we find that existing GAN based anomaly detection algorithms perform poorly due to their inability to handle multimodal patterns. For this purpose we introduce an infinite Gaussian mixture model coupled with (bi-directional) generative adversarial networks, IGMM-GAN, that facilitates multimodal anomaly detection. We illustrate our methodology and its improvement over existing GAN anomaly detection on the MNIST dataset.
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    Coupled IGMM-GANs with Applications to Anomaly Detection in Human Mobility Data
    (ACM, 2020-12) Smolyak, Daniel; Gray, Kathryn; Badirli, Sarkhan; Mohler, George; Computer and Information Science, School of Science
    Detecting anomalous activity in human mobility data has a number of applications, including road hazard sensing, telematics-based insurance, and fraud detection in taxi services and ride sharing. In this article, we address two challenges that arise in the study of anomalous human trajectories: (1) a lack of ground truth data on what defines an anomaly and (2) the dependence of existing methods on significant pre-processing and feature engineering. Although generative adversarial networks (GANs) seem like a natural fit for addressing these challenges, we find that existing GAN-based anomaly detection algorithms perform poorly due to their inability to handle multimodal patterns. For this purpose, we introduce an infinite Gaussian mixture model coupled with (bidirectional) GANs—IGMM-GAN—that is able to generate synthetic, yet realistic, human mobility data and simultaneously facilitates multimodal anomaly detection. Through the estimation of a generative probability density on the space of human trajectories, we are able to generate realistic synthetic datasets that can be used to benchmark existing anomaly detection methods. The estimated multimodal density also allows for a natural definition of outlier that we use for detecting anomalous trajectories. We illustrate our methodology and its improvement over existing GAN anomaly detection on several human mobility datasets, along with MNIST.
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    Tumor infiltrating lymphocyte stratification of prognostic staging of early-stage triple negative breast cancer
    (Springer, 2022-01-11) Loi, Sherene; Salgado, Roberto; Adams, Sylvia; Pruneri, Giancarlo; Francis, Prudence A.; Lacroix-Triki, Magali; Joensuu, Heikki; Dieci, Maria Vittoria; Badve, Sunil; Demaria, Sandra; Gray, Robert; Munzone, Elisabetta; Drubay, Damien; Lemonnier, Jerome; Sotiriou, Christos; Kellokumpu-Lehtinen, Pirkko Liisa; Vingiani, Andrea; Gray, Kathryn; André, Fabrice; Denkert, Carsten; Piccart, Martine; Roblin, Elvire; Michiels , Stefan; Surgery, School of Medicine
    The importance of integrating biomarkers into the TNM staging has been emphasized in the 8th Edition of the American Joint Committee on Cancer (AJCC) Staging system. In a pooled analysis of 2148 TNBC-patients in the adjuvant setting, TILs are found to strongly up and downstage traditional pathological-staging in the Pathological and Clinical Prognostic Stage Groups from the AJJC 8th edition Cancer Staging System. This suggest that clinical and research studies on TNBC should take TILs into account in addition to stage, as for example patients with stage II TNBC and high TILs have a better outcome than patients with stage I and low TILs.
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    Tumor infiltrating lymphocyte stratification of prognostic staging of early-stage triple negative breast cancer
    (Springer Nature, 2022-01-11) Loi, Sherene; Salgado, Roberto; Adams, Sylvia; Pruneri, Giancarlo; Francis, Prudence A.; Lacroix-Triki, Magali; Joensuu, Heikki; Dieci, Maria Vittoria; Badve, Sunil; Demaria, Sandra; Gray, Robert; Munzone, Elisabetta; Drubay, Damien; Lemonnier, Jerome; Sotiriou, Christos; Kellokumpu-Lehtinen, Pirkko Liisa; Vingiani, Andrea; Gray, Kathryn; André, Fabrice; Denkert, Carsten; Piccart, Martine; Roblin, Elvire; Michiels, Stefan; Pathology and Laboratory Medicine, School of Medicine
    The importance of integrating biomarkers into the TNM staging has been emphasized in the 8th Edition of the American Joint Committee on Cancer (AJCC) Staging system. In a pooled analysis of 2148 TNBC-patients in the adjuvant setting, TILs are found to strongly up and downstage traditional pathological-staging in the Pathological and Clinical Prognostic Stage Groups from the AJJC 8th edition Cancer Staging System. This suggest that clinical and research studies on TNBC should take TILs into account in addition to stage, as for example patients with stage II TNBC and high TILs have a better outcome than patients with stage I and low TILs.