6 papers
MRIComp4Flow: Compression of 3D Brain MRI for Training Multi-Modal Generative Models
Lisa K. Fischer, Mykhailo Riabets, Daniel Rueckert +3
Large-scale multi-modal MRI datasets impose substantial storage and I/O costs, limiting the training of 3D generative models on commodity infrastructure. While lossy compression is…
TumorFlow: Physics-Guided Longitudinal MRI Synthesis of Glioblastoma Growth
Valentin Biller, Niklas Bubeck, Lucas Zimmer +6
Glioblastoma exhibits diverse, infiltrative, and patient-specific growth patterns that are only partially visible on routine MRI, making it difficult to reliably assess true tumor…
A Biophysically-Conditioned Generative Framework for 3D Brain Tumor MRI Synthesis
Valentin Biller, Lucas Zimmer, Ayhan Can Erdur +4
Magnetic resonance imaging (MRI) inpainting supports numerous clinical and research applications. We introduce the first generative model that conditions on voxel-level, continuous…
Fast & Efficient Normalizing Flows and Applications of Image Generative Models
Sandeep Nagar
This thesis presents novel contributions in two primary areas: advancing the efficiency of generative models, particularly normalizing flows, and applying generative models to solv…
Parallel Backpropagation for Inverse of a Convolution with Application to Normalizing Flows
Sandeep Nagar, Girish Varma
The inverse of an invertible convolution is an important operation that comes up in Normalizing Flows, Image Deblurring, etc. The naive algorithm for backpropagation of this operat…
R2I-rPPG: A Robust Region of Interest Selection Method for Remote Photoplethysmography to Extract Heart Rate
Sandeep Nagar, Mark Hasegawa-Johnson, David G. Beiser +1
The COVID-19 pandemic has underscored the need for low-cost, scalable approaches to measuring contactless vital signs, either during initial triage at a healthcare facility or virt…