Zobrazeno 1 - 10
of 593
pro vyhledávání: '"Schnabel, Julia"'
Physics-inspired regularization is desired for intra-patient image registration since it can effectively capture the biomechanical characteristics of anatomical structures. However, a major challenge lies in the reliance on physical parameters: Param
Externí odkaz:
http://arxiv.org/abs/2407.04355
Autor:
Avci, Mehmet Yigit, Chan, Emily, Zimmer, Veronika, Rueckert, Daniel, Wiestler, Benedikt, Schnabel, Julia A., Bercea, Cosmin I.
With the increasing incidence of neurodegenerative diseases such as Alzheimer's Disease (AD), there is a need for further research that enhances detection and monitoring of the diseases. We present MORPHADE (Morphological Autoencoders for Alzheimer's
Externí odkaz:
http://arxiv.org/abs/2407.03863
Autor:
Highton, Jack, Chong, Quok Zong, Finestone, Samuel, Beqiri, Arian, Schnabel, Julia A., Bhatia, Kanwal K.
Deep learning models for medical image segmentation and object detection are becoming increasingly available as clinical products. However, as details are rarely provided about the training data, models may unexpectedly fail when cases differ from th
Externí odkaz:
http://arxiv.org/abs/2406.19557
Publikováno v:
Machine.Learning.for.Biomedical.Imaging. 2 (2024)
Pathological lymph node delineation is crucial in cancer diagnosis, progression assessment, and treatment planning. The MICCAI 2023 Lymph Node Quantification Challenge published the first public dataset for pathological lymph node segmentation in the
Externí odkaz:
http://arxiv.org/abs/2406.14365
Interpretability is essential in medical imaging to ensure that clinicians can comprehend and trust artificial intelligence models. Several approaches have been recently considered to encode attributes in the latent space to enhance its interpretabil
Externí odkaz:
http://arxiv.org/abs/2406.08282
Autor:
Spieker, Veronika, Eichhorn, Hannah, Stelter, Jonathan K., Huang, Wenqi, Braren, Rickmer F., Rückert, Daniel, Costabal, Francisco Sahli, Hammernik, Kerstin, Prieto, Claudia, Karampinos, Dimitrios C., Schnabel, Julia A.
Neural implicit k-space representations have shown promising results for dynamic MRI at high temporal resolutions. Yet, their exclusive training in k-space limits the application of common image regularization methods to improve the final reconstruct
Externí odkaz:
http://arxiv.org/abs/2404.08350
Autor:
Li, Jun, Bercea, Cosmin I., Müller, Philip, Felsner, Lina, Kim, Suhwan, Rueckert, Daniel, Wiestler, Benedikt, Schnabel, Julia A.
Unsupervised anomaly detection enables the identification of potential pathological areas by juxtaposing original images with their pseudo-healthy reconstructions generated by models trained exclusively on normal images. However, the clinical interpr
Externí odkaz:
http://arxiv.org/abs/2404.07622
Autor:
Koch, Valentin, Wagner, Sophia J., Kazeminia, Salome, Sancar, Ece, Hehr, Matthias, Schnabel, Julia, Peng, Tingying, Marr, Carsten
In hematology, computational models offer significant potential to improve diagnostic accuracy, streamline workflows, and reduce the tedious work of analyzing single cells in peripheral blood or bone marrow smears. However, clinical adoption of compu
Externí odkaz:
http://arxiv.org/abs/2404.05022
Autor:
Osuala, Richard, Lang, Daniel, Verma, Preeti, Joshi, Smriti, Tsirikoglou, Apostolia, Skorupko, Grzegorz, Kushibar, Kaisar, Garrucho, Lidia, Pinaya, Walter H. L., Diaz, Oliver, Schnabel, Julia, Lekadir, Karim
Contrast agents in dynamic contrast enhanced magnetic resonance imaging allow to localize tumors and observe their contrast kinetics, which is essential for cancer characterization and respective treatment decision-making. However, contrast agent adm
Externí odkaz:
http://arxiv.org/abs/2403.13890
Autor:
Eichhorn, Hannah, Spieker, Veronika, Hammernik, Kerstin, Saks, Elisa, Weiss, Kilian, Preibisch, Christine, Schnabel, Julia A.
We propose PHIMO, a physics-informed learning-based motion correction method tailored to quantitative MRI. PHIMO leverages information from the signal evolution to exclude motion-corrupted k-space lines from a data-consistent reconstruction. We demon
Externí odkaz:
http://arxiv.org/abs/2403.08298