A5027235988- Radiomics and Machine Learning in Medical Imaging
- AI in cancer detection
- Surgical Simulation and Training
- Anatomy and Medical Technology
- Craniofacial Disorders and Treatments
- Medical Image Segmentation Techniques
- Advanced X-ray and CT Imaging
- Advanced Neural Network Applications
- Fetal and Pediatric Neurological Disorders
- Cleft Lip and Palate Research
- Human Pose and Action Recognition
- Augmented Reality Applications
- Video Surveillance and Tracking Methods
- Medical Imaging and Analysis
- Colorectal Cancer Screening and Detection
- COVID-19 diagnosis using AI
- Lung Cancer Diagnosis and Treatment
- Artificial Intelligence in Healthcare and Education
- Neonatal and fetal brain pathology
- Diagnosis and Treatment of Venous Diseases
- Face recognition and analysis
- 3D Shape Modeling and Analysis
- Domain Adaptation and Few-Shot Learning
- dental development and anomalies
- Medical Imaging Techniques and Applications
Polytechnic Institute of Cávado and Ave
2019-2024
University of Minho
2016-2024
DePaul University
2024
Hospital Israelita Albert Einstein
2024
Universidad Nacional Autónoma de México
2024
University of Lübeck
2024
Barnet and Chase Farm NHS Hospitals Trust
2024
Royal Free London NHS Foundation Trust
2024
Chase Farm Hospital
2024
Stanford University
2024
This report presents the methods and results of Thoracic Auto-Segmentation Challenge organized at 2017 Annual Meeting American Association Physicists in Medicine. The purpose challenge was to provide a benchmark dataset platform for evaluating performance autosegmentation organs risk (OARs) thoracic CT images.
Cardiovascular disease is a major cause of death worldwide. Computed Tomography Coronary Angiography (CTCA) non-invasive method used to evaluate coronary artery disease, as well evaluating and reconstructing heart vessel structures. Reconstructed models have wide array for educational, training research applications such the study diseased non-diseased anatomy, machine learning based risk prediction in-silico in-vitro testing medical devices. However, arteries are difficult image due their...
In-utero fetal MRI is emerging as an important tool in the diagnosis and analysis of developing human brain. Automatic segmentation brain a vital step quantitative prenatal neurodevelopment both research clinical context. However, manual cerebral structures time-consuming prone to error inter-observer variability. Therefore, we organized Fetal Tissue Annotation (FeTA) Challenge 2021 order encourage development automatic algorithms on international level. The challenge utilized FeTA Dataset,...
Abstract Chronic Venous Disorders (CVD) of the lower limbs are one most prevalent medical conditions, affecting 35% adults in Europe and North America. Due to exponential growth aging population worsening CVD with age, it is expected that healthcare costs resources needed for treatment will increase coming years. The early diagnosis fundamental planning, while monitoring its assess a patient’s condition quantify evolution CVD. However, correct relies on qualitative approach through visual...
Breast cancer is the most prevalent in world and fifth-leading cause of cancer-related death. Treatment effective early stages. Thus, a need to screen considerable portions population crucial. When screening procedure uncovers suspect lesion, biopsy performed assess its potential for malignancy. This usually using real-time Ultrasound (US) imaging. work proposes visualization system US breast biopsy. It consists an application running on AR glasses that interact with computer application....
Objective: The study proposes to analyze the perception of social responsibility in gastronomy from perspective sustainable development goals (SDGs) during and after COVID-19 pandemic. Theoretical framework: During pandemic, gastronomy, focused on food nutritional security, gained importance. By adopting practices can become a positive force for society, contributing fairer more future. Applying Sustainable Development Goals restaurants not only meets demands conscious customers but also...
<title>Abstract</title> Artificial intelligence (AI) methods for the automatic detection and quantification of COVID-19 lesions in chest computed tomography (CT) might play an important role monitoring management disease. We organized international challenge competition development comparison AI algorithms this task, which we supported with public data state-of-the-art benchmark methods. Board Certified Radiologists annotated 295 images from two sources (A B) training (n=199, source A),...
Abstract Magnetic resonance (MR) imaging is widely used for assessing infant head and brain development diagnosing pathologies. The main goal of this work the a segmentation framework to create patient-specific anatomical models from MR images clinical evaluation. proposed strategy consists fusion-based Deep Learning (DL) approach that combines information different image sequences within acquisition protocol, including axial T1w, sagittal coronal T1w after contrast. These are as input...
Misfit is a risk factor for rehabilitation with implants, and its detection of fundamental importance to the success treatment implants. The use appropriate radiographic imaging key good prognosis. aim this study was compare efficacy film digital radiographs misfit at implant-abutment interface.Digital conventional (manual automatic processing) radiography performed in seven test specimens, each one different vertical between abutment platform implant. Scanning electron microscopy used...
Landmark labeling in 3D head surfaces is an important and routine task clinical practice to evaluate shape, namely analyze cranial deformities or growth evolution. However, manual still applied, being a tedious time-consuming task, highly prone intra-/inter-observer variability, can mislead the diagnose. Thus, automatic methods for anthropometric landmark detection models have high interest practice. In this paper, novel framework proposed accurately detect landmarks infant's surfaces. The...
Semantic segmentation of anatomical structures in laparoscopic videos is a crucial task to enable the development new computer-assisted systems that can assist surgeons during surgery. However, this difficult due artifacts and similar visual characteristics on videos. Recently, deep learning algorithms have been showed promising results instruments. lack large public datasets for semantic structures, there are only few studies task. In work, we evaluate performance five networks, namely...
Ultrasound (US) imaging is a widely used medical modality for the diagnosis, monitoring, and surgical planning kidney conditions. Thus, accurate segmentation of internal structures in US images essential assessment function detection pathological conditions, such as cysts, tumors, stones. Therefore, there need automated methods that can accurately segment images. Over years, automatic strategies were proposed purpose, with deep learning achieving current state-of-the-art results. However,...