Výsledky vyhledávání - "Hamiltonian Variational Autoencoder"

Discriminative Hamiltonian variational autoencoder for accurate tumor segmentation in data-scarce regimes

Autoři: Aghiles Kebaili Jérôme Lapuyade-Lahorgue Pierre Vera a další

Přispěvatelé: Aghiles Kebaili Jérôme Lapuyade-Lahorgue Pierre Vera a další

Zdroj: Neurocomputing. 606:128360

Témata: FOS: Computer and information sciences, 0301 basic medicine, [SDV]Life Sciences [q-bio], Computer Vision and Pattern Recognition (cs.CV), Image and Video Processing (eess.IV), Computer Science - Computer Vision and Pattern Recognition, Deep learning Data augmentation Tumor segmentation Generative modeling Variational Autoencoder MRI PET Hamiltonian Variational Autoencoder, 02 engineering and technology, Electrical Engineering and Systems Science - Image and Video Processing, 03 medical and health sciences, FOS: Electrical engineering, electronic engineering, information engineering, 0202 electrical engineering, electronic engineering, information engineering

Přístupová URL adresa: http://arxiv.org/abs/2406.11659

Discriminative Hamiltonian Variational Autoencoder for Accurate Tumor Segmentation in Data-Scarce Regimes

Autoři: Kebaili, Aghiles Lapuyade-Lahorgue, Jérôme Vera, Pierre a další

Témata: Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition, text

URL: http://arxiv.org/abs/2406.11659

V‐UNet: Medical Image Segmentation Based on Variational Attention Mechanism.

Autoři: Zhang, Yang Yang, Qiang Li, Tian a další

Zdroj: CAAI Transactions on Intelligence Technology; Oct2025, Vol. 10 Issue 5, p1350-1362, 13p

Témata: IMAGE segmentation, COMPUTER-aided diagnosis, BAYESIAN analysis, ROBUST statistics, DEEP learning

End-to-end autoencoding architecture for the simultaneous generation of medical images and corresponding segmentation masks

Autoři: Kebaili, Aghiles Lapuyade-Lahorgue, Jérôme Vera, Pierre a další

Témata: Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition, text

URL: http://arxiv.org/abs/2311.10472

Quasi-symplectic Langevin Variational Autoencoder

Autoři: Wang, Zihao Delingette, Hervé

Témata: Statistics - Machine Learning, Computer Science - Artificial Intelligence, Computer Science - Machine Learning, text

URL: http://arxiv.org/abs/2009.01675

Enhanced Multilinear PCA for Efficient Image Analysis and Dimensionality Reduction: Unlocking the Potential of Complex Image Data.

Autoři: Sun, Tianyu He, Lang Fang, Xi a další

Zdroj: Mathematics (2227-7390); Feb2025, Vol. 13 Issue 3, p531, 16p

Témata: IMAGE recognition (Computer vision), SINGULAR value decomposition, IMAGE analysis, PRINCIPAL components analysis, IMAGE segmentation

Learning the Shape of Evolutionary Landscapes: Geometric Deep Learning Reveals Hidden Structure in Phenotype-to-Fitness Maps

Autoři: Manuel Razo-Mejia Madhav Mani Dmitri A. Petrov

End-to-End Autoencoding Architecture for the Simultaneous Generation of Medical Images and Corresponding Segmentation Masks

Autoři: Kebaili, Aghiles Lapuyade-Lahorgue, Jérôme Vera, Pierre a další

Přispěvatelé: Kebaili, Aghiles Lapuyade-Lahorgue, Jérôme Vera, Pierre a další

Zdroj: Lecture Notes in Electrical Engineering ISBN: 9789819713349

Témata: [SDV] Life Sciences [q-bio], FOS: Computer and information sciences, Computer Vision and Pattern Recognition (cs.CV), Image and Video Processing (eess.IV), Computer Science - Computer Vision and Pattern Recognition, FOS: Electrical engineering, electronic engineering, information engineering, Electrical Engineering and Systems Science - Image and Video Processing

Přístupová URL adresa: http://arxiv.org/abs/2311.10472
https://hal.science/hal-04364812v1

Generative schemes for drug design with shape captioning.

Autoři: Shasya, Shikhar Sharma, Shubham Bhimalapuram, Prabhakar

Zdroj: Journal of Chemical Sciences; Sep2023, Vol. 135 Issue 3, p1-12, 12p

Quasi-Symplectic Langevin Variational Autoencoder

Autoři: Wang, Zihao Delingette, Hervé E-Patient : Images, données & mOdèles pour la médeciNe numériquE (EPIONE) a další

Přispěvatelé: Wang, Zihao Delingette, Hervé E-Patient : Images, données & mOdèles pour la médeciNe numériquE (EPIONE) a další

Zdroj: https://inria.hal.science/hal-03024748 ; 2021.

Témata: [INFO]Computer Science [cs], [MATH]Mathematics [math]

Dostupnost: https://inria.hal.science/hal-03024748
https://inria.hal.science/hal-03024748v4/document
https://inria.hal.science/hal-03024748v4/file/V2_Langevin_VAE_Revision__Copy_.pdf

QUASI-SYMPLECTIC LANGEVIN VARIATIONAL AU-TOENCODER

Autoři: Wang, Zihao Delingette, Hervé E-Patient : Images, données & mOdèles pour la médeciNe numériquE (EPIONE) a další

Přispěvatelé: Wang, Zihao Delingette, Hervé E-Patient : Images, données & mOdèles pour la médeciNe numériquE (EPIONE) a další

Zdroj: https://hal.inria.fr/hal-03024748 ; 2020.

Témata: 41A05, 41A10, 65D05, 65D17 Automatic landmark detec-, [INFO]Computer Science [cs], [MATH]Mathematics [math]

Relation: hal-03024748; https://hal.inria.fr/hal-03024748; https://hal.inria.fr/hal-03024748/document; https://hal.inria.fr/hal-03024748/file/ICLR_Langevin_VAE.pdf

Dostupnost: https://hal.inria.fr/hal-03024748
https://hal.inria.fr/hal-03024748/document
https://hal.inria.fr/hal-03024748/file/ICLR_Langevin_VAE.pdf

Proceedings of 2023 International Conference on Medical Imaging and Computer-Aided Diagnosis (MICAD 2023)

Autoři: Ruidan Su Yu-Dong Zhang Alejandro F. Frangi

Resource Type: eBook.

Témata: Image processing—Digital techniques, Computer vision, Application software

Categories: COMPUTERS / Artificial Intelligence / Computer Vision & Pattern Recognition, COMPUTERS / Business & Productivity Software / General, COMPUTERS / Image Processing

Elements of Dimensionality Reduction and Manifold Learning

Autoři: Benyamin Ghojogh Mark Crowley Fakhri Karray a další

Resource Type: eBook.

Témata: Machine learning--Mathematics, Dimension reduction (Statistics), Machine learning--Statistical methods

Categories: COMPUTERS / Artificial Intelligence / General

System and method for homomorphic compression using latent space preprocessing and neural upsampling