Robert Martí
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View article: Alkaline loading of extracellular vesicles produced from human neural stem cell-derived neurospheres enables CNS drug delivery
Alkaline loading of extracellular vesicles produced from human neural stem cell-derived neurospheres enables CNS drug delivery Open
The blood brain barrier and blood tumor barrier (BBB and BTB, respectively) represent significant obstacles for the delivery of drugs to treat diseases of the central nervous system, such as brain cancers and neurodegenerative diseases. Ex…
View article: Deep learning framework for vertebral heart size and cardiothoracic ratio estimation in dogs and cats using thoracic radiographs
Deep learning framework for vertebral heart size and cardiothoracic ratio estimation in dogs and cats using thoracic radiographs Open
Introduction Heart disease is a major cause of mortality in aging dogs and cats, with cardiomegaly being the most frequent radiographic finding. While deep learning methods have shown potential in detecting and quantifying cardiomegaly, th…
View article: MRI Breast tissue segmentation using nnU-Net for biomechanical modeling
MRI Breast tissue segmentation using nnU-Net for biomechanical modeling Open
Integrating 2D mammography with 3D magnetic resonance imaging (MRI) is crucial for improving breast cancer diagnosis and treatment planning. However, this integration is challenging due to differences in imaging modalities and the need for…
View article: Graph Neural Networks for modelling breast biomechanical compression
Graph Neural Networks for modelling breast biomechanical compression Open
Breast compression simulation is essential for accurate image registration from 3D modalities to X-ray procedures like mammography. It accounts for tissue shape and position changes due to compression, ensuring precise alignment and improv…
View article: PSFHS Challenge Report: Pubic Symphysis and Fetal Head Segmentation from Intrapartum Ultrasound Images
PSFHS Challenge Report: Pubic Symphysis and Fetal Head Segmentation from Intrapartum Ultrasound Images Open
Segmentation of the fetal and maternal structures, particularly intrapartum ultrasound imaging as advocated by the International Society of Ultrasound in Obstetrics and Gynecology (ISUOG) for monitoring labor progression, is a crucial firs…
View article: MAM-E: Mammographic Synthetic Image Generation with Diffusion Models
MAM-E: Mammographic Synthetic Image Generation with Diffusion Models Open
Generative models are used as an alternative data augmentation technique to alleviate the data scarcity problem faced in the medical imaging field. Diffusion models have gathered special attention due to their innovative generation approac…
View article: MAM-E: Mammographic synthetic image generation with diffusion models
MAM-E: Mammographic synthetic image generation with diffusion models Open
Generative models are used as an alternative data augmentation technique to alleviate the data scarcity problem faced in the medical imaging field. Diffusion models have gathered special attention due to their innovative generation approac…
View article: Special Section Guest Editorial: Introduction to the JMI Special Issue on Advances in Breast Imaging
Special Section Guest Editorial: Introduction to the JMI Special Issue on Advances in Breast Imaging Open
The editorial introduces the JMI Special Issue on Advances in Breast Imaging.
View article: Federated Model Aggregation via Self-Supervised Priors for Highly Imbalanced Medical Image Classification
Federated Model Aggregation via Self-Supervised Priors for Highly Imbalanced Medical Image Classification Open
In the medical field, federated learning commonly deals with highly imbalanced datasets, including skin lesions and gastrointestinal images. Existing federated methods under highly imbalanced datasets primarily focus on optimizing a global…
View article: FoPro-KD: Fourier Prompted Effective Knowledge Distillation for Long-Tailed Medical Image Recognition
FoPro-KD: Fourier Prompted Effective Knowledge Distillation for Long-Tailed Medical Image Recognition Open
Representational transfer from publicly available models is a promising technique for improving medical image classification, especially in long-tailed datasets with rare diseases. However, existing methods often overlook the frequency-dep…
View article: Multi-vendor robustness analysis of a commercial artificial intelligence system for breast cancer detection
Multi-vendor robustness analysis of a commercial artificial intelligence system for breast cancer detection Open
The results suggest that the AI system analyzed in our work has a robust diagnostic capability, and that its accuracy is independent of the studied parameters.
View article: A Competition, Benchmark, Code, and Data for Using Artificial Intelligence to Detect Lesions in Digital Breast Tomosynthesis
A Competition, Benchmark, Code, and Data for Using Artificial Intelligence to Detect Lesions in Digital Breast Tomosynthesis Open
Importance An accurate and robust artificial intelligence (AI) algorithm for detecting cancer in digital breast tomosynthesis (DBT) could significantly improve detection accuracy and reduce health care costs worldwide. Objectives To make t…
View article: BUS‐Set: A benchmark for quantitative evaluation of breast ultrasound segmentation networks with public datasets
BUS‐Set: A benchmark for quantitative evaluation of breast ultrasound segmentation networks with public datasets Open
Purpose BUS‐Set is a reproducible benchmark for breast ultrasound (BUS) lesion segmentation, comprising of publicly available images with the aim of improving future comparisons between machine learning models within the field of BUS. Meth…
View article: Brickognize: Applying Photo-Realistic Image Synthesis for Lego Bricks Recognition with Limited Data
Brickognize: Applying Photo-Realistic Image Synthesis for Lego Bricks Recognition with Limited Data Open
During the last few years, supervised deep convolutional neural networks have become the state-of-the-art for image recognition tasks. Nevertheless, their performance is severely linked to the amount and quality of the training data. Acqui…
View article: Visual Attention and Color Cues for 6D Pose Estimation on Occluded Scenarios Using RGB-D Data
Visual Attention and Color Cues for 6D Pose Estimation on Occluded Scenarios Using RGB-D Data Open
Recently, 6D pose estimation methods have shown robust performance on highly cluttered scenes and different illumination conditions. However, occlusions are still challenging, with recognition rates decreasing to less than 10% for half-vis…
View article: A U-Net Ensemble for breast lesion segmentation in DCE MRI
A U-Net Ensemble for breast lesion segmentation in DCE MRI Open
Dynamic Contrast Enhanced Magnetic Resonance Imaging (DCE-MRI) has been recognized as an effective tool for Breast Cancer (BC) diagnosis. Automatic BC analysis from DCE-MRI depends on features extracted particularly from lesions, hence, le…
View article: Challenges of Deep Learning Methods for COVID-19 Detection Using Public Datasets
Challenges of Deep Learning Methods for COVID-19 Detection Using Public Datasets Open
A large number of studies in the past months have proposed deep learning-based Artificial Intelligence (AI) tools for automated detection of COVID-19 using publicly available datasets of Chest X-rays (CXRs) or CT scans for training and eva…
View article: Quality analysis of DCGAN-generated mammography lesions
Quality analysis of DCGAN-generated mammography lesions Open
Medical image synthesis has gained a great focus recently, especially after the introduction of Generative Adversarial Networks (GANs). GANs have been used widely to provide anatomically-plausible and diverse samples for augmentation and o…
View article: Deep learning for mass detection in Full Field Digital Mammograms
Deep learning for mass detection in Full Field Digital Mammograms Open
In recent years, the use of Convolutional Neural Networks (CNNs) in medical imaging has shown improved performance in terms of mass detection and classification compared to current state-of-the-art methods. This paper proposes a fully auto…
View article: DCGANs for realistic breast mass augmentation in x-ray mammography
DCGANs for realistic breast mass augmentation in x-ray mammography Open
Early detection of breast cancer has a major contribution to curability, and using mammographic images, this can be achieved non-invasively. Supervised deep learning, the dominant CADe tool currently, has played a great role in object dete…
View article: Special Section Guest Editorial: Advances in Breast Imaging
Special Section Guest Editorial: Advances in Breast Imaging Open
This guest editorial introduces the special section on Advances in Breast Imaging.
View article: Breast MRI and X-ray mammography registration using gradient values
Breast MRI and X-ray mammography registration using gradient values Open
Breast magnetic resonance imaging (MRI) and X-ray mammography are two image modalities widely used for early detection and diagnosis of breast diseases in women. The combination of these modalities, traditionally done using intensity-based…
View article: Automatic mass detection in mammograms using deep convolutional neural networks
Automatic mass detection in mammograms using deep convolutional neural networks Open
With recent advances in the field of deep learning, the use of convolutional neural networks (CNNs) in medical imaging has become very encouraging. The aim of our paper is to propose a patch-based CNN method for automated mass detection in…
View article: Breast ultrasound lesions recognition: end-to-end deep learning approaches
Breast ultrasound lesions recognition: end-to-end deep learning approaches Open
Multistage processing of automated breast ultrasound lesions recognition is dependent on the performance of prior stages. To improve the current state of the art, we propose the use of end-to-end deep learning approaches using fully convol…