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pro vyhledávání: '"Styner, Martin"'
Anomaly detection and localization in medical imaging remain critical challenges in healthcare. This paper introduces Spatial-MSMA (Multiscale Score Matching Analysis), a novel unsupervised method for anomaly localization in volumetric brain MRIs. Bu
Externí odkaz:
http://arxiv.org/abs/2407.00148
Publikováno v:
LNCS14227(2023)358-368
Functional brain dynamics is supported by parallel and overlapping functional network modes that are associated with specific neural circuits. Decomposing these network modes from fMRI data and finding their temporal characteristics is challenging du
Externí odkaz:
http://arxiv.org/abs/2306.03088
We propose Gumbel Noise Score Matching (GNSM), a novel unsupervised method to detect anomalies in categorical data. GNSM accomplishes this by estimating the scores, i.e. the gradients of log likelihoods w.r.t.~inputs, of continuously relaxed categori
Externí odkaz:
http://arxiv.org/abs/2304.03220
Autor:
Osika, Tom, Ebrahim, Ebrahim, Styner, Martin, Niethammer, Marc, Sawyer, Thomas, Enquobahrie, Andinet
A major data pre-processing step for large, multi-site studies is to handle site effects by harmonizing data, generating a dataset that enables more powerful analyses and more robust algorithms. There is a wide variety of data harmonization technique
Externí odkaz:
http://arxiv.org/abs/2211.07869
Autor:
Gerber, Samuel, Niethammer, Marc, Ebrahim, Ebrahim, Piven, Joseph, Dager, Stephen R., Styner, Martin, Aylward, Stephen, Enquobahrie, Andinet
Brain pathologies often manifest as partial or complete loss of tissue. The goal of many neuroimaging studies is to capture the location and amount of tissue changes with respect to a clinical variable of interest, such as disease progression. Morpho
Externí odkaz:
http://arxiv.org/abs/2208.05891
We present our method for gestational age at birth prediction for the SLCN (surface learning for clinical neuroimaging) challenge. Our method is based on a multi-view shape analysis technique that captures 2D renderings of a 3D object from different
Externí odkaz:
http://arxiv.org/abs/2207.04130
Recent self-supervised advances in medical computer vision exploit global and local anatomical self-similarity for pretraining prior to downstream tasks such as segmentation. However, current methods assume i.i.d. image acquisition, which is invalid
Externí odkaz:
http://arxiv.org/abs/2206.04281
Autor:
Maddock, Richard J., Vlasova, Roza M., Chen, Shuai, Iosif, Ana-Maria, Bennett, Jeffrey, Tanase, Costin, Ryan, Amy M., Murai, Takeshi, Hogrefe, Casey E., Schumann, Cynthia D., Geschwind, Daniel H., Van de Water, Judy, Amaral, David G., Lesh, Tyler A., Styner, Martin A., Kimberley McAllister, A., Carter, Cameron S., Bauman, Melissa D.
Publikováno v:
In Brain Behavior and Immunity October 2024 121:280-290
Autor:
Gibson, Kathryn, Cernasov, Paul, Styner, Martin, Walsh, Erin C., Kinard, Jessica L., Kelley, Lisalynn, Bizzell, Joshua, Phillips, Rachel, Pfister, Courtney, Scott, McRae, Freeman, Louise, Pisoni, Angela, Nagy, Gabriela A., Oliver, Jason A., Smoski, Moria J., Dichter, Gabriel S.
Publikováno v:
In Journal of Affective Disorders 15 September 2024 361:128-138
Autor:
Demers, Catherine H., Hankin, Benjamin L., Haase, Mercedes Hoeflich, Todd, Erin, Hoffman, M. Camille, Epperson, C. Neill, Styner, Martin A., Davis, Elysia Poggi
Publikováno v:
In Journal of Affective Disorders 15 December 2024 367:49-57