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Medical Imaging (algorithm) PhD Projects, Programs & Scholarships

We have 10 Medical Imaging (algorithm) PhD Projects, Programs & Scholarships

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  Medical image-based computational modelling and an AI algorithm development for lung disease diagnosis (MKUF2019)
  Dr K Kim, Dr T Kim
Application Deadline: 31 January 2020

Funding Type

PhD Type

Medical imaging is a clinically established tool to assess structural or (and) functional abnormalities in a respiratory system. It may provide valuable information beyond the human senses.
  Spatial statistical methods and machine learning for analysis of retinal images of optic disc
  Dr G Czanner, Dr S Czanner
Applications accepted all year round

Funding Type

PhD Type

Automated disease diagnosis using medical imaging data is an increasingly important problem. For example, colour retinal images are used to detect glaucoma - a major cause of blindness.
  Development of Convolutional Neural Networks and Associated Error Estimation Techniques for Vertebral Segmentation in Axial Computed Tomography Image Slices
  Dr N Thacker, Dr P Bromiley
Applications accepted all year round

Funding Type

PhD Type

Osteoporosis is a common skeletal disorder that increases the risk of fractures and causes significant morbidity and mortality. Vertebral fractures (VFs) are often an early manifestation of osteoporosis but are significantly under-diagnosed in clinical practice.
  Functional imaging of liver using contrast enhanced ultrasound
  Dr M Tang, Dr K Christensen, Dr Y Luan
Applications accepted all year round

Funding Type

PhD Type

This project is part of the joint King's College London - Imperial College London EPSRC Centre for Doctoral Training (CDT) in Smart Medical Imaging.
  Functional imaging of liver using contrast enhanced ultrasound
  Dr M Tang, Dr K Christensen, Dr Y Luan
Applications accepted all year round

Funding Type

PhD Type

This project is part of the joint King's College London - Imperial College London EPSRC Centre for Doctoral Training (CDT) in Smart Medical Imaging.
  Computer-aided Detection of Osteoporotic Vertebral Fractures in Clinical Images Using Convolutional Neural Network Constrained Local Models (CNN-CLMs)
  Dr P Bromiley, Prof T Cootes, Dr EP Kariki
Applications accepted all year round

Funding Type

PhD Type

Osteoporosis is a common, degenerative skeletal disorder that increases the risk of fractures and causes significant morbidity and mortality.
  Development of image analysis methods and software to study musculoskeletal diseases using artificial intelligence (AI)
  Dr Claudia Lindner, Prof T Cootes
Applications accepted all year round

Funding Type

PhD Type

Musculoskeletal (MSK) diseases affect an increasing number of the population globally. In the UK, on average 3 in 10 people are affected, which increases to 1 in 2 in the age group 65+.
  Personalised surgery for colorectal cancer cure by use of fluorescence guidance- increasing safety & cancer precision
  Dr R Cahill
Applications accepted all year round

Funding Type

PhD Type

Surgery is the best treatment for colorectal cancer, most especially for cure. It remains a major operation with considerable risks meaning that every effort should be made to ensure maximum safety and effectiveness in obtaining a complete resection.
  Novel Camera Technology for Imaging of Cell Collectives - Start date: 1st October 2019
  Prof D Faccio
Applications accepted all year round

Funding Type

PhD Type

The University of Glasgow (UofG) is home to world-leading research in the fields of imaging and computing science. UofG is also home to the UK Hub for Quantum Imaging Technologies, QuantIC (https://quantic.ac.uk/) who are co-funding this PhD research project in collaboration with a Horiba.
  Non coding RNAs in Adrenocortical Cancer
  Prof G. Hutvagner
Applications accepted all year round

Funding Type

PhD Type

Background. Industry, society or clinical need for project and scientific rationale. Adrenocortical carcinoma (ACC) is a rare disease which occurs with an annual incidence of 2-4 cases per million with a five-year survival of less than 16-38%.
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