Zobrazeno 1 - 10
of 561
pro vyhledávání: '"parra, lucas"'
Autor:
Velarde, Osvaldo M, Parra, Lucas C
Conventional computer vision models rely on very deep, feedforward networks processing whole images and trained offline with extensive labeled data. In contrast, biological vision relies on comparatively shallow, recurrent networks that analyze seque
Externí odkaz:
http://arxiv.org/abs/2411.16695
Students often drift in and out of focus during class. Effective teachers recognize this and re-engage them when necessary. With the shift to remote learning, teachers have lost the visual feedback needed to adapt to varying student engagement. We pr
Externí odkaz:
http://arxiv.org/abs/2409.13084
Geometric Deep Learning (GDL) unifies a broad class of machine learning techniques from the perspectives of symmetries, offering a framework for introducing problem-specific inductive biases like Graph Neural Networks (GNNs). However, the current for
Externí odkaz:
http://arxiv.org/abs/2408.15894
Complex systems, such as brains, markets, and societies, exhibit internal dynamics influenced by external factors. Disentangling delayed external effects from internal dynamics within these systems is often challenging. We propose using a Vector Auto
Externí odkaz:
http://arxiv.org/abs/2404.10834
Autor:
Hirsch, Lukas, Huang, Yu, Makse, Hernan A., Martinez, Danny F., Hughes, Mary, Eskreis-Winkler, Sarah, Pinker, Katja, Morris, Elizabeth, Parra, Lucas C., Sutton, Elizabeth J.
Women with an increased life-time risk of breast cancer undergo supplemental annual screening MRI. We propose to predict the risk of developing breast cancer within one year based on the current MRI, with the objective of reducing screening burden an
Externí odkaz:
http://arxiv.org/abs/2312.00067
Autor:
Velarde, Osvaldo Matias, Parra, Lucas
Deep neural models have shown remarkable performance in image recognition tasks, whenever large datasets of labeled images are available. The largest datasets in radiology are available for screening mammography. Recent reports, including in high imp
Externí odkaz:
http://arxiv.org/abs/2206.12407
Publikováno v:
In Brain Stimulation May-June 2024 17(3):561-571
Radiologist-level Performance by Using Deep Learning for Segmentation of Breast Cancers on MRI Scans
Autor:
Hirsch, Lukas, Huang, Yu, Luo, Shaojun, Saccarelli, Carolina Rossi, Gullo, Roberto Lo, Naranjo, Isaac Daimiel, Bitencourt, Almir G. V., Onishi, Natsuko, Ko, Eun Sook, Leithner, Doris, Avendano, Daly, Eskreis-Winkler, Sarah, Hughes, Mary, Martinez, Danny F., Pinker, Katja, Juluru, Krishna, El-Rowmeim, Amin E., Elnajjar, Pierre, Morris, Elizabeth A., Makse, Hernan A., Parra, Lucas C, Sutton, Elizabeth J.
Purpose: To develop a deep network architecture that would achieve fully automated radiologist-level segmentation of cancers at breast MRI. Materials and Methods: In this retrospective study, 38229 examinations (composed of 64063 individual breast sc
Externí odkaz:
http://arxiv.org/abs/2009.09827
Purpose: Conventional automated segmentation of the head anatomy in MRI distinguishes different brain and non-brain tissues based on image intensities and prior tissue probability maps (TPM). This works well for normal head anatomies, but fails in th
Externí odkaz:
http://arxiv.org/abs/1905.10010