2 citations · 5 across the 8 of their papers we have counts for
9 papers
VampPrior Unleashed
Andrew A. Stirn, Abhejit Rajagopal, David A. Knowles +1
Deep latent variable models (DLVMs) underpin many modern analysis pipelines, for example in single-cell genomics, yet their prior distributions have received markedly little attent…
A Mixed Precision FFT with applications in MRI
Nikhil Deveshwar, Abhejit Rajagopal, Peder E. Z. Larson
A mixed precision Fast Fourier transform (FFT) implementation is presented. The procedure uses per-block microscaling (MX), a global power-of-two prescale, and prequantized low bit…
Mixed Supervision of Histopathology Improves Prostate Cancer Classification from MRI
Abhejit Rajagopal, Antonio C. Westphalen, Nathan Velarde +6
Non-invasive prostate cancer detection from MRI has the potential to revolutionize patient care by providing early detection of clinically-significant disease (ISUP grade group >=…
Physics-driven Deep Learning for PET/MRI
Abhejit Rajagopal, Andrew P. Leynes, Nicholas Dwork +3
In this paper, we review physics- and data-driven reconstruction techniques for simultaneous positron emission tomography (PET) / magnetic resonance imaging (MRI) systems, which ha…
Synthetic PET via Domain Translation of 3D MRI
Abhejit Rajagopal, Yutaka Natsuaki, Kristen Wangerin +7
Historically, patient datasets have been used to develop and validate various reconstruction algorithms for PET/MRI and PET/CT. To enable such algorithm development, without the ne…
Federated Learning with Research Prototypes for Multi-Center MRI-based Detection of Prostate Cancer with Diverse Histopathology
Abhejit Rajagopal, Ekaterina Redekop, Anil Kemisetti +5
Early prostate cancer detection and staging from MRI are extremely challenging tasks for both radiologists and deep learning algorithms, but the potential to learn from large and d…
Predicting Generalization in Deep Learning via Local Measures of Distortion
Abhejit Rajagopal, Vamshi C. Madala, Shivkumar Chandrasekaran +1
We study generalization in deep learning by appealing to complexity measures originally developed in approximation and information theory. While these concepts are challenged by th…
Fast Algorithms for Displacement and Low-Rank Structured Matrices
Shivkumar Chandrasekaran, Nithin Govindarajan, Abhejit Rajagopal
This tutorial provides an introduction to the development of fast matrix algorithms based on the notions of displacement and various low-rank structures.
Deep Algorithms: designs for networks
Abhejit Rajagopal, Shivkumar Chandrasekaran, Hrushikesh N. Mhaskar
A new design methodology for neural networks that is guided by traditional algorithm design is presented. To prove our point, we present two heuristics and demonstrate an algorithm…