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of 43
pro vyhledávání: '"Barnett, Alina"'
Autor:
Willard, Frank, Moffett, Luke, Mokel, Emmanuel, Donnelly, Jon, Guo, Stark, Yang, Julia, Kim, Giyoung, Barnett, Alina Jade, Rudin, Cynthia
Prototypical-part models are a popular interpretable alternative to black-box deep learning models for computer vision. However, they are difficult to train, with high sensitivity to hyperparameter tuning, inhibiting their application to new datasets
Externí odkaz:
http://arxiv.org/abs/2406.14675
Autor:
Yang, Julia, Barnett, Alina Jade, Donnelly, Jon, Kishore, Satvik, Fang, Jerry, Schwartz, Fides Regina, Chen, Chaofan, Lo, Joseph Y., Rudin, Cynthia
Digital mammography is essential to breast cancer detection, and deep learning offers promising tools for faster and more accurate mammogram analysis. In radiology and other high-stakes environments, uninterpretable ("black box") deep learning models
Externí odkaz:
http://arxiv.org/abs/2406.06386
Autor:
Tang, Dennis, Willard, Frank, Tegerdine, Ronan, Triplett, Luke, Donnelly, Jon, Moffett, Luke, Semenova, Lesia, Barnett, Alina Jade, Jing, Jin, Rudin, Cynthia, Westover, Brandon
In electroencephalogram (EEG) recordings, the presence of interictal epileptiform discharges (IEDs) serves as a critical biomarker for seizures or seizure-like events.Detecting IEDs can be difficult; even highly trained experts disagree on the same s
Externí odkaz:
http://arxiv.org/abs/2312.10056
Autor:
Barnett, Alina Jade, Guo, Zhicheng, Jing, Jin, Ge, Wendong, Kaplan, Peter W., Kong, Wan Yee, Karakis, Ioannis, Herlopian, Aline, Jayagopal, Lakshman Arcot, Taraschenko, Olga, Selioutski, Olga, Osman, Gamaleldin, Goldenholz, Daniel, Rudin, Cynthia, Westover, M. Brandon
Publikováno v:
NEJM AI. 2024 Jun; 1(6): 10.1056/aioa2300331
In intensive care units (ICUs), critically ill patients are monitored with electroencephalograms (EEGs) to prevent serious brain injury. The number of patients who can be monitored is constrained by the availability of trained physicians to read EEGs
Externí odkaz:
http://arxiv.org/abs/2211.05207
Publikováno v:
2022 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR)
We present a deformable prototypical part network (Deformable ProtoPNet), an interpretable image classifier that integrates the power of deep learning and the interpretability of case-based reasoning. This model classifies input images by comparing t
Externí odkaz:
http://arxiv.org/abs/2111.15000
Autor:
Barnett, Alina Jade, Schwartz, Fides Regina, Tao, Chaofan, Chen, Chaofan, Ren, Yinhao, Lo, Joseph Y., Rudin, Cynthia
When we deploy machine learning models in high-stakes medical settings, we must ensure these models make accurate predictions that are consistent with known medical science. Inherently interpretable networks address this need by explaining the ration
Externí odkaz:
http://arxiv.org/abs/2107.05605
Autor:
Barnett, Alina Jade, Schwartz, Fides Regina, Tao, Chaofan, Chen, Chaofan, Ren, Yinhao, Lo, Joseph Y., Rudin, Cynthia
Interpretability in machine learning models is important in high-stakes decisions, such as whether to order a biopsy based on a mammographic exam. Mammography poses important challenges that are not present in other computer vision tasks: datasets ar
Externí odkaz:
http://arxiv.org/abs/2103.12308
Publikováno v:
Advances in Neural Information Processing Systems 32 (NeurIPS 2019)
When we are faced with challenging image classification tasks, we often explain our reasoning by dissecting the image, and pointing out prototypical aspects of one class or another. The mounting evidence for each of the classes helps us make our fina
Externí odkaz:
http://arxiv.org/abs/1806.10574
Akademický článek
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Autor:
Barnett, Alina Jade, Guo, Zhicheng, Jing, Jin, Ge, Wendong, Rudin, Cynthia, Westover, M. Brandon
In intensive care units (ICUs), critically ill patients are monitored with electroencephalograms (EEGs) to prevent serious brain injury. The number of patients who can be monitored is constrained by the availability of trained physicians to read EEGs
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5ac2347ce121e0d7ce6dedf243563a3e
http://arxiv.org/abs/2211.05207
http://arxiv.org/abs/2211.05207