Postdoc en el area de cancer de pulmon y deep learning (9 meses) - Escocia

Detailed Description
Autofluorescence lifetime images (FLIM) reveal unique characteristics of endogenous fluorescence in biological samples, but comprehensive interrogation still relies on histology images. Co-registering these images, however, is extremely challenging due to their different nature.

The lack of availability of the ground truth for the co-registration impedes the application of many registration technologies. Our research group is interested in the development of unsupervised image-to-image translation networks that significantly improves the success of the co-registration using a conventional optimisation-based regression network. Our preliminary results also indicate that the approach is flexible enough to be applied to various image formats, for example, intensity images, showing the great potential of spectral lifetime images for rapid visual discrimination of lung cancer.

Since direct correlation of histology images with intensity/lifetime images, e.g., least square, is hardly applicable, even though human intervention is introduced, our current main interest is twofold. First, to investigate methods that could limit the currently required human intervention by finding automatic correlations and, second, to study other advanced registration techniques that are able to surpass the regression. Additionally, we found out that our developed model is highly sensitive to hyperparameter tuning, which implies that performance improvement through a more thorough search can be achieved.

Key Duties and Responsibilities

• Designing and training deep learning models to address the challenges of image-to-image transformations.
• Write and/or contribute to academic articles (of suitable quality to be returned in the Research Excellence Framework) and contribute to other publications arising out of the project.
• Assisting with the filing of intellectual property and technology transfer.
• Supervising the activities of junior group members and PhD students.
• Participate in regular project meetings and participate in research networks more broadly.
• Develop innovative approaches to presentation and engagement, delivering research seminars and disseminating research findings and carrying out impact activities as appropriate.
• Participate in and/or take the lead on (as appropriate) the writing of new follow-on research proposals that build on the results of the project.

More Information
https://enzj.fa.em3.oraclecloud.com/hcmUI/CandidateExperience/en/sites/CX/job/1753