Domenec Puig

Professor

Universitat Rovira i Virgili

Spain

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About

Domenec Puig is a Professor at Universitat Rovira i Virgili in Spain. His research focuses on areas such as segmentation techniques in computer vision, deep learning architectures for medical imaging, and advanced prediction models using multimodal data. Recent articles highlight his contributions to depth estimation, neonatal birth weight prediction, and tumor classification in medical imaging.

Articles (17)

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2025

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2025

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2025

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2025

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2025

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2024

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2024

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2024

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2022

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2022

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2022

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2022

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2021

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2021

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2021

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2020

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2020

Collaborators (11)

Farhan Akram

Erasmus University Rotterdam

NETHERLANDS

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Estefania Talavera Martinez

University of Twente

NETHERLANDS

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Petia Radeva

Full professor

University of Barcelona

SPAIN

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Santiago Romani

Lecturer in Comupter Science and Tecnology

Universitat Rovira i Virgili

SPAIN

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Mohamed Abdel-Nasser

SPAIN

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Michelle Murphy

Profesora Agregat

Universitat Rovira i Virgili

SPAIN

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Fatemeh Vafaee

Associate Professor

University of New South Wales

AUSTRALIA

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Thanh Thi Nguyen

Associate Professor

MONASH UNIVERSITY

AUSTRALIA

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Moona Mazher

Universitat Rovira i Virgili

SPAIN

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Md. Mostafa Kamal Sarker

University of Oxford

UNITED KINGDOM

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Blas Herrera

Profesor Titular de Universidad / Full University Professor of Applied Mathematics

Universitat Rovira i Virgili

SPAIN

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Domenec Puig

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Articles (17)

Annotation-Efficient and Domain-General Segmentation from Weak Labels: A Bounding Box-Guided Approach

CoHAtNet: An integrated convolutional-transformer architecture with hybrid self-attention for end-to-end camera localization

DAR-MDE: Depth-Attention Refinement for Multi-Scale Monocular Depth Estimation

Efficient crack segmentation with multi-decoder networks and enhanced feature fusion

Interpretable deep neural networks for advancing early neonatal birth weight prediction using multimodal maternal factors

Dimension Reduction of Multidimensional Structured and Unstructured Datasets through Ensemble Learning of Neural Embeddings

Classifying Breast Tumors in Digital Tomosynthesis by Combining Image Quality-Aware Features and Tumor Texture Descriptors

Maternal Nutritional Factors Enhance Birthweight Prediction: A Super Learner Ensemble Approach

Effective Approaches to Fetal Brain Segmentation in MRI and Gestational Age Estimation by Utilizing a Multiview Deep Inception Residual Network and Radiomics

An Enhanced Scheme for Reducing the Complexity of Pointwise Convolutions in CNNs for Image Classification Based on Interleaved Grouped Filters without Divisibility Constraints

Predicting Breast Tumor Malignancy Using Deep ConvNeXt Radiomics and Quality-Based Score Pooling in Ultrasound Sequences

3D-IncNet: Head and Neck (H&amp;N) Primary Tumors Segmentation and Survival Prediction

AWEU-Net: An Attention-Aware Weight Excitation U-Net for Lung Nodule Segmentation

SLSNet: Skin lesion segmentation using a lightweight generative adversarial network

LungINFseg: Segmenting COVID-19 Infected Regions in Lung CT Images Based on a Receptive-Field-Aware Deep Learning Framework

Hierarchical Approach to Classify Food Scenes in Egocentric Photo-Streams

Fully automated breast density segmentation and classification using deep learning

Collaborators (11)

Farhan Akram

Estefania Talavera Martinez

Petia Radeva

Santiago Romani

Mohamed Abdel-Nasser

Michelle Murphy

Fatemeh Vafaee

Thanh Thi Nguyen

Moona Mazher

Md. Mostafa Kamal Sarker

Blas Herrera

Make your next move.

Domenec Puig

Research Interests

Explore related searches

Contact this professor

About

Articles (17)

Annotation-Efficient and Domain-General Segmentation from Weak Labels: A Bounding Box-Guided Approach

CoHAtNet: An integrated convolutional-transformer architecture with hybrid self-attention for end-to-end camera localization

DAR-MDE: Depth-Attention Refinement for Multi-Scale Monocular Depth Estimation

Efficient crack segmentation with multi-decoder networks and enhanced feature fusion

Interpretable deep neural networks for advancing early neonatal birth weight prediction using multimodal maternal factors

Dimension Reduction of Multidimensional Structured and Unstructured Datasets through Ensemble Learning of Neural Embeddings

Classifying Breast Tumors in Digital Tomosynthesis by Combining Image Quality-Aware Features and Tumor Texture Descriptors

Maternal Nutritional Factors Enhance Birthweight Prediction: A Super Learner Ensemble Approach

Effective Approaches to Fetal Brain Segmentation in MRI and Gestational Age Estimation by Utilizing a Multiview Deep Inception Residual Network and Radiomics

An Enhanced Scheme for Reducing the Complexity of Pointwise Convolutions in CNNs for Image Classification Based on Interleaved Grouped Filters without Divisibility Constraints

Predicting Breast Tumor Malignancy Using Deep ConvNeXt Radiomics and Quality-Based Score Pooling in Ultrasound Sequences

3D-IncNet: Head and Neck (H&amp;amp;N) Primary Tumors Segmentation and Survival Prediction

AWEU-Net: An Attention-Aware Weight Excitation U-Net for Lung Nodule Segmentation

SLSNet: Skin lesion segmentation using a lightweight generative adversarial network

LungINFseg: Segmenting COVID-19 Infected Regions in Lung CT Images Based on a Receptive-Field-Aware Deep Learning Framework

Hierarchical Approach to Classify Food Scenes in Egocentric Photo-Streams

Fully automated breast density segmentation and classification using deep learning

Collaborators (11)

Farhan Akram

Estefania Talavera Martinez

Petia Radeva

Santiago Romani

Mohamed Abdel-Nasser

Michelle Murphy

Fatemeh Vafaee

Thanh Thi Nguyen

Moona Mazher

Md. Mostafa Kamal Sarker

Blas Herrera

Make your next move.

3D-IncNet: Head and Neck (H&N) Primary Tumors Segmentation and Survival Prediction