A5031248347- Fetal and Pediatric Neurological Disorders
- Radiomics and Machine Learning in Medical Imaging
- Domain Adaptation and Few-Shot Learning
- Advanced Neural Network Applications
- COVID-19 diagnosis using AI
- Neonatal and fetal brain pathology
- Medical Imaging Techniques and Applications
- Brain Tumor Detection and Classification
- Spinal Dysraphism and Malformations
- AI in cancer detection
- Medical Image Segmentation Techniques
- Advanced Neuroimaging Techniques and Applications
- Medical Imaging and Analysis
- Stochastic Gradient Optimization Techniques
- Advanced Radiotherapy Techniques
- Advanced X-ray and CT Imaging
- Adversarial Robustness in Machine Learning
- Cancer-related molecular mechanisms research
- Congenital Diaphragmatic Hernia Studies
- Assisted Reproductive Technology and Twin Pregnancy
- COVID-19 Clinical Research Studies
- Advanced MRI Techniques and Applications
- Neonatal Respiratory Health Research
- Cerebrospinal fluid and hydrocephalus
- Digital Imaging for Blood Diseases
King's College School
2024
King's College London
2019-2023
Consorci Institut D'Investigacions Biomediques August Pi I Sunyer
2023
Kings Health Partners
2023
St Thomas' Hospital
2023
Tanta University
2023
Technical University of Munich
2022
University Children's Hospital Zurich
2022
University of Zurich
2022
University Hospital of Zurich
2022
Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrotic core, active non-enhancing core. This intrinsic heterogeneity is also portrayed in their radio-phenotype, as sub-regions depicted by varying intensity profiles disseminated across multi-parametric magnetic resonance imaging (mpMRI) scans, reflecting biological properties....
Medical image analysis and computer-assisted intervention problems are increasingly being addressed with deep-learning-based solutions. Established deep-learning platforms flexible but do not provide specific functionality for medical adapting them this domain of application requires substantial implementation effort. Consequently, there has been duplication effort incompatible infrastructure developed across many research groups. This work presents the open-source NiftyNet platform deep...
Computational pathology is revolutionizing the field of by integrating advanced computer vision and machine learning technologies into diagnostic workflows. It offers unprecedented opportunities for improved efficiency in treatment decisions allowing pathologists to achieve higher precision objectivity disease classification, tumor microenvironment description identification new biomarkers. However, potential computational personalized medicine comes with significant challenges, particularly...
Real-time tool segmentation from endoscopic videos is an essential part of many computer-assisted robotic surgical systems and critical importance in data science. We propose two novel deep learning architectures for automatic non-rigid instruments. Both methods take advantage automated deep-learning-based multi-scale feature extraction while trying to maintain accurate quality at all resolutions. The proposed encode the constraint inside network architecture. first architecture enforces it...
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,...
Deep learning models for medical image segmentation can fail unexpectedly and spectacularly pathological cases images acquired at different centers than training images, with labeling errors that violate expert knowledge. Such undermine the trustworthiness of deep segmentation. Mechanisms detecting correcting such failures are essential safely translating this technology into clinics likely to be a requirement future regulations on artificial intelligence (AI). In work, we propose...
Spina bifida aperta (SBA) is a birth defect associated with severe anatomical changes in the developing fetal brain. Brain magnetic resonance imaging (MRI) atlases are popular tools for studying neuropathology brain anatomy, but previous MRI have focused on normal We aimed to develop spatio-temporal atlas SBA.
In fetal Magnetic Resonance Imaging, Super Resolution Reconstruction (SRR) algorithms are becoming popular tools to obtain high-resolution 3D volume reconstructions from low-resolution stacks of 2D slices, acquired at different orientations. To be effective, these often require accurate segmentation the region interest, such as brain in suspected pathological cases. case Spina Bifida, Ebner, Wang et al. (NeuroImage, 2020) combined their SRR algorithm with a 2-step pipeline (2D localisation...
A retrospective study was performed to the effect of fetal surgery on brain development measured by MRI in fetuses with myelomeningocele (MMC).
Congenital diaphragmatic hernia is associated with high mortality and morbidity, including evidence suggesting neurodevelopmental comorbidities after birth. The aim of this study was to document longitudinal changes in brain biometry the cortical folding pattern fetuses congenital compared healthy fetuses.
Recent research on COVID-19 suggests that CT imaging provides useful information to assess disease progression and assist diagnosis, in addition help understanding the disease. There is an increasing number of studies propose use deep learning provide fast accurate quantification using chest scans. The main tasks interest are automatic segmentation lung lesions scans confirmed or suspected patients. In this study, we compare twelve algorithms a multi-center dataset, including both...
<ns4:p><ns4:bold>Background:</ns4:bold> Spina bifida aperta (SBA) is a birth defect associated with severe anatomical changes in the developing fetal brain. Brain magnetic resonance imaging (MRI) atlases are popular tools for studying neuropathology brain anatomy, but previous MRI have focused on normal We aimed to develop spatio-temporal atlas SBA.</ns4:p><ns4:p> </ns4:p><ns4:p> <ns4:bold>Methods:</ns4:bold> developed semi-automatic computational method compute first SBA. used 90 MRIs of...
Producing manual, pixel-accurate, image segmentation labels is tedious and time-consuming. This often a rate-limiting factor when large amounts of labeled images are required, such as for training deep convolutional networks instrument-background in surgical scenes. No datasets comparable to industry standards the computer vision community available this task. To circumvent problem, we propose automate creation realistic dataset by exploiting techniques stemming from special effects...
Limiting failures of machine learning systems is paramount importance for safety-critical applications. In order to improve the robustness systems, Distributionally Robust Optimization (DRO) has been proposed as a generalization Empirical Risk Minimization (ERM). However, its use in deep severely restricted due relative inefficiency optimizers available DRO comparison wide-spread variants Stochastic Gradient Descent (SGD) ERM.<br>We propose SGD with hardness weighted sampling,...