A5081518328- Blood groups and transfusion
- Advanced Radiotherapy Techniques
- Erythrocyte Function and Pathophysiology
- Medical Imaging and Analysis
- Medical Imaging Techniques and Applications
- Hemoglobinopathies and Related Disorders
- Chronic Lymphocytic Leukemia Research
- Radiomics and Machine Learning in Medical Imaging
- Blood Coagulation and Thrombosis Mechanisms
- Blood disorders and treatments
- Clinical Laboratory Practices and Quality Control
- Lymphoma Diagnosis and Treatment
- Advanced X-ray and CT Imaging
- Advanced Neural Network Applications
- Chronic Myeloid Leukemia Treatments
- Acute Myeloid Leukemia Research
- Platelet Disorders and Treatments
- Medical Image Segmentation Techniques
- Blood transfusion and management
- Blood properties and coagulation
- Trauma, Hemostasis, Coagulopathy, Resuscitation
- Venous Thromboembolism Diagnosis and Management
- Neonatal Health and Biochemistry
- Immune Cell Function and Interaction
- Iron Metabolism and Disorders
University of Manchester
2022-2024
The Christie NHS Foundation Trust
2022
Provincial Laboratory of Public Health
2010
McMaster University
2010
Health Sciences Centre
2010
Alberta Health Services
2010
London Health Sciences Centre
2010
St. Jude Children's Research Hospital
2010
Université de Sherbrooke
2005
County Hospital
1987-1990
Radiation therapy plays a crucial role in cancer treatment, necessitating precise delivery of radiation to tumors while sparing healthy tissues over multiple days. Computed tomography (CT) is integral for treatment planning, offering electron density data accurate dose calculations. However, accurately representing patient anatomy challenging, especially adaptive radiotherapy, where CT not acquired daily. Magnetic resonance imaging (MRI) provides superior soft-tissue contrast. Still, it...
Missing tissue presents a big challenge for dose mapping, e.g., in the reirradiation setting. We propose pipeline to identify missing on intra-patient structure meshes using previously trained geometric-learning correspondence model. For our application, we relied prediction discrepancies between forward and backward correspondences of input meshes, quantified correspondence-based Inverse Consistency Error (cICE). optimised threshold applied cICE points dataset 35 simulated mandible...
Automatic segmentation of organs-at-risk in radiotherapy planning computed tomography (CT) scans using convolutional neural networks (CNNs) is an active research area. Very large datasets are usually required to train such CNN models. In radiotherapy, large, high-quality scarce and combining data from several sources can reduce the consistency training segmentations. It therefore important understand impact quality on performance auto-segmentation models for radiotherapy.
Patients with limited-stage small-cell carcinoma of the lung (SCCL) were randomly assigned to a four-drug chemotherapy program consisting methotrexate, doxorubicin, cyclophosphamide, and CCNU (MACC) or regimen CCNU, vincristine alternated Adriamycin (Adria Laboratories, Columbus, Ohio) (CCV/AV). All patients received 4,500 cGy, in split course, primary tumor, mediastinum, supraclavicular lymph node drainage areas 3,000 cGy whole brain. After four cycles chemotherapy, plus methanol...
Journal Article SOME PRELIMINARY OBSERVATIONS ON THE TREATMENT OF RHEUMATOID ARTHRITIS WITH CORTISONE PLUS INSULIN Get access The of Clinical Endocrinology & Metabolism, Volume 10, Issue 7, 1 July 1950, Pages 800–802, https://doi.org/10.1210/jcem-10-7-800 Published: 01 1950
Patient education is an important component of the management chronic diseases such as SLE. We have investigated value World Wide Web a medium for delivery SLE patient information. Volunteers recruited from clinic and website completed interviews questionnaires aimed at defining their information needs. A new was then established its impact on users tested using knowledge questionnaires. The used extensively (20–30 each day) over 24 month period study until April 2001. total 510 participants...
Skeletal muscle segmentation is an important procedure for assessing sarcopenia, emerging imaging biomarker of patient frailty. Data annotation remains the bottleneck training deep learning auto-segmentation models.There a need to define methodologies applying models different domains (e.g., anatomical regions or modalities) without dramatically increasing data annotation.To address this problem, we empirically evaluate generalizability various source tasks transfer learning: natural image...