Ali Bashashati Saghezchi
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- Familiarize yourself with program requirements. You want to learn as much as possible from the information available to you before you reach out to a faculty member. Be sure to visit the graduate degree program listing and program-specific websites.
- Check whether the program requires you to seek commitment from a supervisor prior to submitting an application. For some programs this is an essential step while others match successful applicants with faculty members within the first year of study. This is either indicated in the program profile under "Admission Information & Requirements" - "Prepare Application" - "Supervision" or on the program website.
- Identify specific faculty members who are conducting research in your specific area of interest.
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- Read up on the faculty members in the program and the research being conducted in the department.
- Familiarize yourself with their work, read their recent publications and past theses/dissertations that they supervised. Be certain that their research is indeed what you are hoping to study.
- Compose an error-free and grammatically correct email addressed to your specifically targeted faculty member, and remember to use their correct titles.
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ADVICE AND INSIGHTS FROM UBC FACULTY ON REACHING OUT TO SUPERVISORS
These videos contain some general advice from faculty across UBC on finding and reaching out to a potential thesis supervisor.
Supervision Enquiry
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Theses completed in 2010 or later are listed below. Please note that there is a 6-12 month delay to add the latest theses.
The full abstract for this thesis is available in the body of the thesis, and will be available when the embargo expires.
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Ovarian carcinoma is the deadliest cancer of the female reproductive system in North America. There are five major histological subtypes which require different treatments. Pathologists diagnose these histotypes by examining hematoxylin and eosin (H&E)-stained whole slide images (WSIs) of tissue. However, histotype diagnosis is not simple, with poor interobserver agreement between general pathologists (Cohen’s kappa 0.54-0.67). We hypothesize that latest machine learning (ML)-based image classification models may be able to recognize ovarian carcinoma histotype sufficiently well that they could aid pathologists in diagnosis.However, the color variation of H&E-stained tissues, especially those from different centers/hospitals, is a longstanding challenge for applications of AI in digital pathology. First, we investigate eight color normalization algorithms as a preprocessing step for artificial intelligence (AI)-based classification. Using multiple datasets of different cancer types, reference images, and cross-validation splits, we show that color normalization significantly improves the classification accuracy of WSIs when the train and test data are from separate institutions (ovarian cancer: 0.25 AUC increase, p = 1.6 e-05, pleural cancer: 0.21 AUC increase, p = 1.4 e-10). Furthermore, we introduce a novel augmentation strategy by mixing color-normalized images using three easily accessible algorithms that consistently improves the diagnosis of test images from external institutions.Secondly, we train four different deep convolutional neural networks to automatically classify H&E-stained images of epithelial ovarian carcinoma using the largest training dataset to date (948slides corresponding to 485 patients). Performance is assessed on an independent test set of 60 patients from another institution. The best performing model achieves a mean diagnostic concordance of 80.97% (Cohen’s kappa 0.7547). As well, in 4 of 8 cases misclassified by ML from the external dataset, two expert subspecialty pathologists rendered diagnoses, based on blind review of the WSIs, that agree with AI rather than the integrated reference diagnosis.Our results indicate that color normalization can reliably improve AI-based diagnosis of WSIs sourced from multiple centers, and specifically that an ML-based ovarian carcinoma classifier is ready for clinical validation studies as an adjunct for informing histotype diagnosis, thereby supporting histotype-specific ovarian cancer treatment and accordingly reduce the deadliness of this disease.
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The full abstract for this thesis is available in the body of the thesis, and will be available when the embargo expires.
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