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J. Iglesias

4 papers hereh-index 4129 citations24 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • middle author1
  • last author2

Across the 3 of 4 papers where every author was matched, so the position is known.

fields
  • eess.IV2
  • cs.CV1
  • cs.LG1
same name
  • J. Iglesias — 1 paper, h 7
  • J. Iglesias — 1 paper, h 1
  • J. Iglesias — 1 paper, h 12
  • J. Iglesias — 1 paper, h 6

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20242026
collaborators

4 papers

cs.CV2026

Enhanced Portable Ultra Low-Field Diffusion Tensor Imaging with Bayesian Artifact Correction and Deep Learning-Based Super-Resolution

Mark D. Olchanyi, Annabel Sorby-Adams, John Kirsch +7

Portable, ultra-low-field (ULF) magnetic resonance imaging has the potential to expand access to neuroimaging but currently suffers from coarse spatial and angular resolutions and…

eess.IV2025

TV-based Deep 3D Self Super-Resolution for fMRI

Fernando Pérez-Bueno, Hongwei Bran Li, Matthew S. Rosen +3

While functional Magnetic Resonance Imaging (fMRI) offers valuable insights into cognitive processes, its inherent spatial limitations pose challenges for detailed analysis of the…

eess.IV2024

H-SynEx: Using synthetic images and ultra-high resolution ex vivo MRI for hypothalamus subregion segmentation

Livia Rodrigues, Martina Bocchetta, Oula Puonti +6

The hypothalamus is a small structure located in the center of the brain and is involved in significant functions such as sleeping, temperature, and appetite control. Various neuro…

cs.LG2024

Probabilistic Contrastive Learning with Explicit Concentration on the Hypersphere

Hongwei Bran Li, Cheng Ouyang, Tamaz Amiranashvili +3

Self-supervised contrastive learning has predominantly adopted deterministic methods, which are not suited for environments characterized by uncertainty and noise. This paper intro…

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