Výsledky vyhledávání - "Mauricio Orbes-Arteaga"

DermX: An end-to-end framework for explainable automated dermatological diagnosis

Autor: Raluca Jalaboi, Frederik Faye, Mauricio Orbes-Arteaga, Dan Jørgensen, Ole Winther, Alfiia Galimzianova

Publikováno v: Jalaboi, R, Faye, F, Orbes-Arteaga, M, Jørgsen, D, Winther, O & Galimzianova, A 2023, ' DermX: An end-to-end framework for explainable automated dermatological diagnosis ', Medical Image Analysis, vol. 83, 102647 . https://doi.org/10.1016/j.media.2022.102647
Jalaboi, R, Faye, F, Orbes-Arteaga, M, Jørgensen, D, Winther, O & Galimzianova, A 2023, ' DermX : An end-to-end framework for explainable automated dermatological diagnosis ', Medical Image Analysis, vol. 83, 102647 . https://doi.org/10.1016/j.media.2022.102647

Dermatological diagnosis automation is essential in addressing the high prevalence of skin diseases and critical shortage of dermatologists. Despite approaching expert-level diagnosis performance, convolutional neural network (ConvNet) adoption in cl

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::eb565c1761cf0854d526273e9cd27216
https://pubmed.ncbi.nlm.nih.gov/36272237

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Explainability of Convolutional Neural Networks for Dermatological Diagnosis (Preprint)

Autor: Raluca Jalaboi, Mauricio Orbes Arteaga, Dan Richter Jørgensen, Ionela Manole, Oana Ionescu Bozdog, Andrei Chiriac, Ole Winther, Alfiia Galimzianova

BACKGROUND Convolutional neural networks (CNNs) are regarded as state-of-the-art artificial intelligence (AI) tools for dermatological diagnosis, and they have been shown to achieve expert-level performance when trained on a representative dataset. C

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::757ec8f03018895530fa3b375d4c0602
https://doi.org/10.2196/preprints.35437

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Multi-domain Adaptation in Brain MRI Through Paired Consistency and Adversarial Learning

Autor: Mauricio Orbes-Arteaga, Parashkev Nachev, Thomas Varsavsky, Lauge Sørensen, Lewis J. Haddow, Sebastien Ourselin, Zach Eaton-Rosen, Akshay Pai, Carole H. Sudre, M. Jorge Cardoso, Mads Nielsen, Marc Modat

Publikováno v: Domain Adaptation and Representation Transfer and Medical Image Learning with Less Labels and Imperfect Data ISBN: 9783030333904
DART/MIL3ID@MICCAI
Domain Adaptation and Representation Transfer and Medical Image Learning with Less Labels and Imperfect Data-First MICCAI Workshop, DART 2019, and First International Workshop, MIL3ID 2019, Shenzhen, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 13 and 17, 2019, Proceedings
Lecture Notes in Computer Science
Lecture Notes in Computer Science-Domain Adaptation and Representation Transfer and Medical Image Learning with Less Labels and Imperfect Data
Domain Adapt Represent Transf Med Image Learn Less Labels Imperfect Data (2019)

Supervised learning algorithms trained on medical images will often fail to generalize across changes in acquisition parameters. Recent work in domain adaptation addresses this challenge and successfully leverages labeled data in a source domain to p

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7443dd9b455c87491ad8888ff3c151c7
https://pubmed.ncbi.nlm.nih.gov/34109324

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Explainability of Convolutional Neural Networks for Dermatological Diagnosis

Autor: Raluca Jalaboi, Mauricio Orbes Arteaga, Dan Richter Jørgensen, Ionela Manole, Oana Ionescu Bozdog, Andrei Chiriac, Ole Winther, Alfiia Galimzianova

Publikováno v: Iproceedings. 6:e35437

Background Convolutional neural networks (CNNs) are regarded as state-of-the-art artificial intelligence (AI) tools for dermatological diagnosis, and they have been shown to achieve expert-level performance when trained on a representative dataset. C

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::e10b1420b439e63aa082eb40f5a3b926
https://doi.org/10.2196/35437

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Evaluation of segmentation methods on head and neck CT: Auto-segmentation challenge 2015

Publikováno v: Medical Physics. 44:2020-2036

Purpose Automated delineation of structures and organs is a key step in medical imaging. However, due to the large number and diversity of structures and the large variety of segmentation algorithms, a consensus is lacking as to which automated segme

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f2cc540e053203a4f9b86dd26a147612
https://doi.org/10.1002/mp.12197

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Test-Time Unsupervised Domain Adaptation

Autor: M. Jorge Cardoso, Carole H. Sudre, Mauricio Orbes-Arteaga, Thomas Varsavsky, Mark S. Graham, Parashkev Nachev

Publikováno v: Medical Image Computing and Computer Assisted Intervention – MICCAI 2020 ISBN: 9783030597092
MICCAI (1)

Convolutional neural networks trained on publicly available medical imaging datasets (source domain) rarely generalise to different scanners or acquisition protocols (target domain). This motivates the active field of domain adaptation. While some ap

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::0d30612788e6a7948b0c4813b128ee4b
https://doi.org/10.1007/978-3-030-59710-8_42

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PADDIT: Probabilistic Augmentation of Data using Diffeomorphic Image Transformation

Autor: Stefan Sommer, Akshay Pai, Jorge Cardoso, Mads Nielsen, Mauricio Orbes-Arteaga, Sebastien Ourselin, Lauge Sørensen, Christian Igel, Marc Modat

Publikováno v: Medical Imaging 2019: Image Processing
Medical Imaging: Image Processing

For proper generalization performance of convolutional neural networks (CNNs) in medical image segmentation, the learnt features should be invariant under particular non-linear shape variations of the input. To induce invariance in CNNs to such trans

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Simultaneous synthesis of FLAIR and segmentation of white matter hypointensities from T1 MRIs

Autor: Mauricio Orbes-Arteaga, Jorge Cardoso, M., Lauge Sørensen, Marc Modat, Sebastien Ourselin, Mads Nielsen, Akshay Pai

Publikováno v: King's College London
Marc Modat

Segmenting vascular pathologies such as white matter lesions in Brain magnetic resonance images (MRIs) require acquisition of multiple sequences such as T1-weighted (T1-w) --on which lesions appear hypointense-- and fluid attenuated inversion recover

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Head and Neck Auto Segmentation Challenge based on Non-Local Generative Models

Autor: Mauricio Orbes Arteaga, David Cárdenas Peña, German Castellanos Dominguez

Publikováno v: The MIDAS Journal.

A new patch based label fusion method based on generative approach is proposed for segmentation of mandible, brainstem, parotid and submandibular glands, optic nerves and the optic chiasm in head and neck CT images. The proposal constructs local clas

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0ba05df6fa33994eaf6b7df6580219e4
https://doi.org/10.54294/pbx1zn

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Kernel Centered Alignment Supervised Metric for Multi-Atlas Segmentation

Autor: David Cárdenas-Peña, Alvaro A. Orozco, Mauricio Orbes-Arteaga, Mauricio A. Álvarez, Germán Castellanos-Domínguez

Publikováno v: Image Analysis and Processing — ICIAP 2015 ISBN: 9783319232300
ICIAP (1)

Recently multi-atlas based methods have been used for supporting brain structure segmentation. These approaches encode the shape variability on a given population and provide prior information. However, the accuracy on the segmentation depend on the

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::f3edb0555762bd4711f8610b02cd0a1a
https://doi.org/10.1007/978-3-319-23231-7_59

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