2 citations · 5 across the 6 of their papers we have counts for
6 papers
Emergence of Fibrations, Compression, and Symmetry Breaking in Artificial Neural Networks
Osvaldo M Velarde, Lucas C Parra, Alireza Hashemi +1
Artificial neural networks are often regarded as powerful yet opaque black boxes. Here, we demonstrate that learning in deep neural networks generates local symmetries known in gra…
Full-Head Segmentation of MRI with Abnormal Brain Anatomy: Model and Data Release
Andrew M Birnbaum, Adam Buchwald, Peter Turkeltaub +7
Purpose: The goal of this work was to develop a deep network for whole-head segmentation including clinical MRIs with abnormal anatomy, and compile the first public benchmark datas…
Recurrent Joint Embedding Predictive Architecture with Recurrent Forward Propagation Learning
Osvaldo M Velarde, Lucas C Parra
Conventional computer vision models rely on very deep, feedforward networks processing whole images and trained offline with extensive labeled data. In contrast, biological vision…
The Role of Fibration Symmetries in Geometric Deep Learning
Osvaldo Velarde, Lucas Parra, Paolo Boldi +1
Geometric Deep Learning (GDL) unifies a broad class of machine learning techniques from the perspectives of symmetries, offering a framework for introducing problem-specific induct…
Predicting breast cancer with AI for individual risk-adjusted MRI screening and early detection
Lukas Hirsch, Yu Huang, Hernan A. Makse +7
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…
Independent evaluation of state-of-the-art deep networks for mammography
Osvaldo Matias Velarde, Lucas Parra
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 avai…