1 citations · 3 across the 9 of their papers we have counts for
9 papers
Influence of Early through Late Fusion on Pancreas Segmentation from Imperfectly Registered Multimodal MRI
Lucas W. Remedios, Han Liu, Samuel W. Remedios +7
Multimodal fusion promises better pancreas segmentation. However, where to perform fusion in models is still an open question. It is unclear if there is a best location to fuse inf…
Data-driven Nucleus Subclassification on Colon H&E using Style-transferred Digital Pathology
Lucas W. Remedios, Shunxing Bao, Samuel W. Remedios +16
Understanding the way cells communicate, co-locate, and interrelate is essential to furthering our understanding of how the body functions. H&E is widely available, however, cell s…
Nucleus subtype classification using inter-modality learning
Lucas W. Remedios, Shunxing Bao, Samuel W. Remedios +14
Understanding the way cells communicate, co-locate, and interrelate is essential to understanding human physiology. Hematoxylin and eosin (H&E) staining is ubiquitously available b…
Super-resolution multi-contrast unbiased eye atlases with deep probabilistic refinement
Ho Hin Lee, Adam M. Saunders, Michael E. Kim +15
Purpose: Eye morphology varies significantly across the population, especially for the orbit and optic nerve. These variations limit the feasibility and robustness of generalizing…
Exploring shared memory architectures for end-to-end gigapixel deep learning
Lucas W. Remedios, Leon Y. Cai, Samuel W. Remedios +8
Deep learning has made great strides in medical imaging, enabled by hardware advances in GPUs. One major constraint for the development of new models has been the saturation of GPU…
Extracting 2D weak labels from volume labels using multiple instance learning in CT hemorrhage detection
Samuel W. Remedios, Zihao Wu, Camilo Bermudez +6
Multiple instance learning (MIL) is a supervised learning methodology that aims to allow models to learn instance class labels from bag class labels, where a bag is defined to cont…
Generalizing Deep Whole Brain Segmentation for Pediatric and Post-Contrast MRI with Augmented Transfer Learning
Camilo Bermudez, Justin Blaber, Samuel W. Remedios +6
Generalizability is an important problem in deep neural networks, especially in the context of the variability of data acquisition in clinical magnetic resonance imaging (MRI). Rec…
Distributed deep learning for robust multi-site segmentation of CT imaging after traumatic brain injury
Samuel Remedios, Snehashis Roy, Justin Blaber +6
Machine learning models are becoming commonplace in the domain of medical imaging, and with these methods comes an ever-increasing need for more data. However, to preserve patient…
Inter-Scanner Harmonization of High Angular Resolution DW-MRI using Null Space Deep Learning
Vishwesh Nath, Prasanna Parvathaneni, Colin B. Hansen +15
Diffusion-weighted magnetic resonance imaging (DW-MRI) allows for non-invasive imaging of the local fiber architecture of the human brain at a millimetric scale. Multiple classical…