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pro vyhledávání: '"Rubin, P. D."'
Weakly supervised learning with noisy data has drawn attention in the medical imaging community due to the sparsity of high-quality disease labels. However, little is known about the limitations of such weakly supervised learning and the effect of th
Externí odkaz:
http://arxiv.org/abs/2402.04419
Myriad uses, methodologies, and channels have been explored for side-channel analysis. However, specific implementation considerations are often unpublished. This paper explores select test configuration and collection parameters, such as input volta
Externí odkaz:
http://arxiv.org/abs/2204.04766
Autor:
Tushar, Fakrul Islam, D'Anniballe, Vincent M., Rubin, Geoffrey D., Samei, Ehsan, Lo, Joseph Y.
Despite the potential of weakly supervised learning to automatically annotate massive amounts of data, little is known about its limitations for use in computer-aided diagnosis (CAD). For CT specifically, interpreting the performance of CAD algorithm
Externí odkaz:
http://arxiv.org/abs/2202.11709
Side-channel analysis, originally used in cryptanalysis is growing in use cases, both offensive and defensive. Wavelet analysis is a commonly employed time-frequency analysis technique used across disciplines, with a variety of purposes, and has show
Externí odkaz:
http://arxiv.org/abs/2107.11870
Autor:
D'Anniballe, Vincent M., Tushar, Fakrul Islam, Faryna, Khrystyna, Han, Songyue, Mazurowski, Maciej A., Rubin, Geoffrey D., Lo, Joseph Y.
Purpose: To develop high throughput multi-label annotators for body (chest, abdomen, and pelvis) Computed Tomography (CT) reports that can be applied across a variety of abnormalities, organs, and disease states. Approach: We used a dictionary approa
Externí odkaz:
http://arxiv.org/abs/2102.02959
Autor:
Saha, Anindo, Tushar, Fakrul I., Faryna, Khrystyna, D'Anniballe, Vincent M., Hou, Rui, Mazurowski, Maciej A., Rubin, Geoffrey D., Lo, Joseph Y.
Weakly supervised disease classification of CT imaging suffers from poor localization owing to case-level annotations, where even a positive scan can hold hundreds to thousands of negative slices along multiple planes. Furthermore, although deep lear
Externí odkaz:
http://arxiv.org/abs/2011.00149
Autor:
Tushar, Fakrul Islam, D'Anniballe, Vincent M., Hou, Rui, Mazurowski, Maciej A., Fu, Wanyi, Samei, Ehsan, Rubin, Geoffrey D., Lo, Joseph Y.
Purpose: To design multi-disease classifiers for body CT scans for three different organ systems using automatically extracted labels from radiology text reports.Materials & Methods: This retrospective study included a total of 12,092 patients (mean
Externí odkaz:
http://arxiv.org/abs/2008.01158
Autor:
Draelos, Rachel Lea, Dov, David, Mazurowski, Maciej A., Lo, Joseph Y., Henao, Ricardo, Rubin, Geoffrey D., Carin, Lawrence
Machine learning models for radiology benefit from large-scale data sets with high quality labels for abnormalities. We curated and analyzed a chest computed tomography (CT) data set of 36,316 volumes from 19,993 unique patients. This is the largest
Externí odkaz:
http://arxiv.org/abs/2002.04752
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