Suchergebnisse - "medical image analysis"

AI-Powered Radiomics for Breast Cancer Prognosis and Aggressiveness Assessment via Ultrasound Imaging

Autoren: Issam Ismail Zaidkilani, Nadeem

Thesis Advisors: García García, Miguel Angel, Puig Valls, Domènec Savi

Quelle: TDX (Tesis Doctorals en Xarxa)

Schlagwörter: Càncer de Mama - Imatges per Ultrasò, Anàlisi d'Imatges Mèdiques, Diagnòstic Assistit per Ordinador (CAD), Cáncer de Mama - Imágenes por Ultrasonido, Análisis de Imágenes Médicas, Diagnóstico Asistido por Computadora (CAD), Breast Cancer-Ultrasound, Medical Image Analysis, Computer-Aided Diagnosis (CAD), Ciències

Dateibeschreibung: application/pdf

Zugangs-URL: http://hdl.handle.net/10803/695647

Advances towards a robust AI-based glioma grading system

Autoren: Pitarch i Abaigar, Carla

Weitere Verfasser: University/Department: Universitat Politècnica de Catalunya. Departament de Ciències de la Computació

Thesis Advisors: Vellido Alcacena, Alfredo, Ribas Ripoll, Vicente Jorge

Quelle: TDX (Tesis Doctorals en Xarxa)

Schlagwörter: glioma grading, artificial intelligence, deep learning, magnetic resonance imaging, neuro-oncology, model robustness, reliable AI, data preprocessing, medical image analysis, Àrees temàtiques de la UPC::Informàtica, Àrees temàtiques de la UPC::Enginyeria biomèdica, 004 - Informàtica, 616.8 - Neurologia. Neuropatologia.Sistema nerviós

Dateibeschreibung: application/pdf

Zugangs-URL: http://hdl.handle.net/10803/694181
https://dx.doi.org/10.5821/dissertation-2117-427440

Deep Learning-based Methods for Extracting Fundus Image Landmarks and Signs of Eye Diseases

Autoren: Ali, Mohammed Yousef Salem

Weitere Verfasser: University/Department: Universitat Rovira i Virgili. Departament d'Enginyeria Informàtica i Matemàtiques

Thesis Advisors: Valls Mateu, Aïda, Abdelnasser Mohamed Mahmoud, Mohamed, Baget Bernaldiz, Marc

Quelle: TDX (Tesis Doctorals en Xarxa)

Schlagwörter: aprenentatge profund, visió per computador, Anàlisi d'imatges mèdiques, aprendizaje profundo, visión por computador, Análisis de imágenes médicas, deep learning, computer vision, Medical image analysis, Enginyeria i arquitectura

Dateibeschreibung: application/pdf

Zugangs-URL: http://hdl.handle.net/10803/687502

A domain adaptation model for carotid ultrasound: image harmonization, noise reduction, and impact on cardiovascular risk markers

Autoren: Usama, Mohd Nyman, Emma Näslund, Ulf et al.

Quelle: Computers in Biology and Medicine. 190

Schlagwörter: Cardiovascular disease assessment, Carotid ultrasound images, Deep learning, Domain adaptation, Generative Adversarial Network, Image harmonization, Medical image analysis, Noise reduction

Dateibeschreibung: electronic

Zugangs-URL: https://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-237445
https://doi.org/10.1016/j.compbiomed.2025.110030

Learning representations for medical image diagnosis: impact of curriculum training and architectural design

Autoren: Jiménez Sánchez, Amelia

Weitere Verfasser: University/Department: Universitat Pompeu Fabra. Departament de Tecnologies de la Informació i les Comunicacions

Thesis Advisors: Piella Fenoy, Gemma, Mateus, Diana

Quelle: TDX (Tesis Doctorals en Xarxa)

Schlagwörter: Learning representations, Medical image analysis, Medical image diagnosis, Deep learning, Capsule nerworks, Curriculum learning, Federated learning, Domain adaptation, Aprendizaje de representaciones, Análisis de imagen médica, Diagnóstico en imagen mèdica, Aprendizaje profundo, Aprendizaje curricular, Aprendizaje federado, Adaptación al dominio

Dateibeschreibung: application/pdf

Zugangs-URL: http://hdl.handle.net/10803/672839

Image Segmentation Methods for Automatic Detection of the Anatomical Structure of the Eye in People with Diabetic Retinopathy

Autoren: Escorcia Gutierrez, José

Weitere Verfasser: University/Department: Universitat Rovira i Virgili. Departament d'Enginyeria Informàtica i Matemàtiques

Thesis Advisors: Puig Valls, Domènec, Romero Aroca, Pedro, Valls Mateu, Aïda

Quelle: TDX (Tesis Doctorals en Xarxa)

Schlagwörter: Retinopatia diabètica, Anàlisi d'imatges mèdiques, Segmentació de la imatge del fons, Retinopatía diabética, Análisis de imágenes médicas, Segmentación de la imagen del fondo de ojo, Diabetic retinopathy, Medical image analysis, Fundus image segmentation, Ingeniería y arquitectura

Time: 621.3

Dateibeschreibung: application/pdf

Zugangs-URL: http://hdl.handle.net/10803/671543

Performance Comparison of YOLOv8 and DETR in White Blood Cell Detection

Autoren: Rakhmatsyah, Andrian Abdurohman, Maman Erfianto, Bayu et al.

Quelle: Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi); Vol 9 No 4 (2025): August 2025; 923-934

Schlagwörter: Transformer, hematology, white blood cell classification, YOLOv8, object detection, DeTR, medical image analysis, data augmentation

Dateibeschreibung: application/pdf

Zugangs-URL: https://jurnal.iaii.or.id/index.php/RESTI/article/view/6864

Comparative analysis of random forest and deep learning approaches for automated acute lymphoblastic leukemia detection using morphologicaland textural features

Autoren: Windra Swastika Kestrilia Rega Prilianti Paulus Lucky Tirma Irawan et al.

Quelle: Jurnal Ilmiah Kursor: Menuju Solusi Teknologi Informasi, Vol 13, Iss 1 (2025)

Schlagwörter: Deep Learning, Random Forest, morphological features, Electronic computers. Computer science, acute lymphoblastic leukemia, QA75.5-76.95, medical image analysis

Zugangs-URL: https://doaj.org/article/949ec8f7c9b540bcb645e50b6df13eca

Multi-stage intermediate fusion for multimodal learning to classify non-small cell lung cancer subtypes from CT and PET

Autoren: Fatih Aksu Fabrizia Gelardi Arturo Chiti et al.

Quelle: Pattern Recognition Letters. 193:86-93

Schlagwörter: FOS: Computer and information sciences, Computer Science - Artificial Intelligence, Computer Vision and Pattern Recognition (cs.CV), Histological subtype classification, Image and Video Processing (eess.IV), Computer Science - Computer Vision and Pattern Recognition, Electrical Engineering and Systems Science - Image and Video Processing, Quantitative Biology - Quantitative Methods, Multi-stage fusion, Artificial Intelligence (cs.AI), Multimodal deep learning, Medical image analysis, FOS: Biological sciences, FOS: Electrical engineering, electronic engineering, information engineering, Intermediate fusion, Quantitative Methods (q-bio.QM)

Zugangs-URL: http://arxiv.org/abs/2501.12425
https://hdl.handle.net/20.500.11768/187784
https://doi.org/10.1016/j.patrec.2025.04.001

Preliminary Results on Automated Binary Classification of Congenital Heart Defects in Fetal Ultrasound Images Using Deep Learning Algorithm

Autoren: Kristina Ćaleta Ana Šarčević Jelena Božek et al.

Quelle: 2025 MIPRO 48th ICT and Electronics Convention. :1310-1315

Schlagwörter: Classification algorithms, Ultrasonic imaging, Medical diagnostic imaging, Congenital Heart Disease, Deep learning, Diseases, Thermal stability, ResNet-18, Fetus, Deep Learning, Medical Image Analysis, Training, Fetal Ultrasound, Accuracy, Prediction algorithms

Robust ROI Detection in Whole Slide Images Guided by Pathologists’ Viewing Patterns

Autoren: Ghezloo, Fatemeh Chang, Oliver H Knezevich, Stevan R et al.

Quelle: Journal of Imaging Informatics in Medicine. 38(1)

Schlagwörter: 32 Biomedical and Clinical Sciences (for-2020), 3202 Clinical Sciences (for-2020), Bioengineering (rcdc), Machine Learning and Artificial Intelligence (rcdc), Networking and Information Technology R&D (NITRD) (rcdc), Clinical Research (rcdc), 4.2 Evaluation of markers and technologies (hrcs-rac), 4.1 Discovery and preclinical testing of markers and technologies (hrcs-rac), Humans (mesh), Deep Learning (mesh), Pathologists (mesh), Melanoma (mesh), Image Interpretation, Computer-Assisted (mesh), Skin Neoplasms (mesh), Image Processing, Computer-Assisted (mesh), Diagnosis, Computer-Assisted (mesh), Digital pathology, Medical image analysis, Deep learning, Region of interest, Saliency detection, Image reconstruction, Humans (mesh), Melanoma (mesh), Skin Neoplasms (mesh), Diagnosis, Computer-Assisted (mesh), Image Interpretation, Computer-Assisted (mesh), Image Processing, Computer-Assisted (mesh), Pathologists (mesh), Deep Learning (mesh), Deep learning, Digital pathology, Image reconstruction, Medical image analysis, Region of interest, Saliency detection, Humans (mesh), Deep Learning (mesh), Pathologists (mesh), Melanoma (mesh), Image Interpretation, Computer-Assisted (mesh), Skin Neoplasms (mesh), Image Processing, Computer-Assisted (mesh), Diagnosis, Computer-Assisted (mesh)

Dateibeschreibung: application/pdf

Zugangs-URL: https://escholarship.org/uc/item/6mg0z4bv
https://escholarship.org/content/qt6mg0z4bv/qt6mg0z4bv.pdf

Swin Transformer Applied to Breast MRI Super-Resolution in a Cross-Cohort Dataset

Autoren: Sousa, Pedro Sousa, Henrique Pereira, Tânia et al.

Quelle: 2025 IEEE 38th International Symposium on Computer-Based Medical Systems (CBMS). :1-6

Schlagwörter: Machine Learning, Breast cancer, Super-Resolution, Medical Image Analysis, Synthetic Image Generation

3D MEDICAL IMAGE SEGMENTATION WITH DEEP LEARNING METHODS

Autoren: Sezin Barın Uçman Ergün Gür Emre Güraksın

Quelle: Volume: 9, Issue: 173-91
International Journal of 3D Printing Technologies and Digital Industry

Schlagwörter: Deep Learning, Image Processing, 3D Image Segmentation, Medical Image Analysis, U-Net, UNETR, Swin-Unet, Yazılım Mühendisliği (Diğer), Software Engineering (Other), 3D U-Net, SwinUNETR

Dateibeschreibung: application/pdf

Zugangs-URL: https://dergipark.org.tr/tr/pub/ij3dptdi/issue/91345/1571288

Preliminary Results on Automated Binary Classification of Congenital Heart Defects in Fetal Ultrasound Images Using Deep Learning Algorithm

Autoren: Horvat, Marko Božek, Jelena Šarčević, Ana et al.

Quelle: 48th ICT and Electronics Convention MIPRO 2025. :1498-1503

Schlagwörter: Deep Learning, Congenital Heart Disease, Medical Image Analysis, Fetal Ultrasound, ResNet-18

Investigating Reinforcement Learning for Histopathological Image Analysis

Autoren: Mohamad, Mohamad Ponzio, Francesco Gassier, Maxime et al.

Weitere Verfasser: Mohamad, Mohamad Ponzio, Francesco Gassier, Maxime et al.

Quelle: Proceedings of the 18th International Joint Conference on Biomedical Engineering Systems and Technologies. :369-375

Schlagwörter: Deep Reinforcement Learning, Medical Image Analysis, Whole Slide Images, Computational Pathology, Goal-Conditioned Reinforcement Learning, [INFO] Computer Science [cs]

Dateibeschreibung: application/pdf

Zugangs-URL: https://hal.science/hal-05130080v1/document
https://hal.science/hal-05130080v1
https://doi.org/10.5220/0013300900003911
https://www.scitepress.org/Link.aspx?doi=10.5220/0013300900003911
https://doi.org/10.5220/0013300900003911
https://hdl.handle.net/11583/3000733

Multi-feature machine learning analysis for an improved characterization of the cardiac mechanics

Autoren: Sánchez Martínez, Sergio

Weitere Verfasser: University/Department: Universitat Pompeu Fabra. Departament de Tecnologies de la Informació i les Comunicacions

Thesis Advisors: Duchateau, Nicolas, Piella Fenoy, Gemma, Bijnens, Bart

Quelle: TDX (Tesis Doctorals en Xarxa)

Schlagwörter: Machine learning, Medical image analysis, Pattern recognition, Multiple kernel learning, Dimensionality reduction, Echocardiography, Early diagnosis, Heart failure, Cardiac resynchronization therapy, Aprendizaje automático, Análisis de imágenes médicas, Reconocimiento de patrones, Aprendizaje de kernel múltiple, Ecocardiografía, Diagnóstico temprano, Insuficiencia cardíaca, Tratamiento de re-sincronización cardíaca

Dateibeschreibung: application/pdf

Zugangs-URL: http://hdl.handle.net/10803/663748

Hybrid ViT–CNN Model for Automatic Monkeypox Skin Lesion Diagnosis

Autoren: Aji Triwerdaya Ema Utami

Quelle: Journal of Electrical Engineering and Computer, Vol 7, Iss 2, Pp 461-470 (2025)

Schlagwörter: monkeypox skin lesions deep learning vision transformers convolutional neural network hybrid model medical image analysis, Electrical engineering. Electronics. Nuclear engineering, TK1-9971

Dateibeschreibung: electronic resource

Relation: https://ejournal.unuja.ac.id/index.php/jeecom/article/view/12795; https://doaj.org/toc/2715-0410; https://doaj.org/toc/2715-6427

Zugangs-URL: https://doaj.org/article/a82ec598deb047e6bfaefe663ab3069b

HSSAM-Net: hyper-scale shifted aggregation network for precise colorectal polyp segmentation in endoscopic images

Autoren: Qing Feng Shahzad Ahmed Yueming Zhang et al.

Quelle: Scientific Reports, Vol 15, Iss 1, Pp 1-21 (2025)

Schlagwörter: Colorectal cancer, Polyp segmentation, Medical image analysis, Attention mechanisms, Deep learning, Medicine, Science

Dateibeschreibung: electronic resource

Relation: https://doaj.org/toc/2045-2322

Zugangs-URL: https://doaj.org/article/7266ee8de1cb4e5f8bf6d60cfd2ac426

A deep learning-based dual-branch framework for automated skin lesion segmentation and classification via dermoscopic Images

Autoren: Hamza Abu Owida Ibrahim Abd El-Fattah Suhaila Abuowaida et al.

Quelle: Scientific Reports, Vol 15, Iss 1, Pp 1-29 (2025)

Schlagwörter: Skin lesion segmentation, Medical image analysis, Deep learning, Efficient_Net, Attention mechanisms, Computer-aided diagnosis, Medicine, Science

Dateibeschreibung: electronic resource

Relation: https://doaj.org/toc/2045-2322

Zugangs-URL: https://doaj.org/article/ed079bcd63364cdd890e441c63e16d5f

Hierarchical multi-scale vision transformer model for accurate detection and classification of brain tumors in MRI-based medical imaging

Autoren: C. Sankari V. Jamuna A. R. Kavitha

Quelle: Scientific Reports, Vol 15, Iss 1, Pp 1-19 (2025)

Schlagwörter: Automated brain tumor detection, Vision transformer architecture, Hierarchical attention mechanisms, Medical image analysis, Deep learning applications, Multi-resolution feature extraction, Medicine, Science

Dateibeschreibung: electronic resource

Relation: https://doaj.org/toc/2045-2322

Zugangs-URL: https://doaj.org/article/e107bbfc78104fe5a24d407ab1627a85