I have a passion for designing machine learning and artificially intelligent models for medicine. We can do more to improve patient care and treatment by leveraging A.I. and I’m excited to be part of that change.
Here you’ll find some of the projects that I have tackled over my career as a graduate student, postdoctoral fellow and now industry researcher. My publications range from tumor segmentation algorithms to neural networks which learns from weak labels. Take a look around!
Shazia Akbar, PhD
|Altis Labs, Toronto (Canada)||2019 – 2020|
|Machine Learning Engineer|
|Sunnybrook Research Institute (Canada)|
Medical Biophysics, University of Toronto
|2016 – 2019|
|New York University, School of Medicine (USA)||2015 – 2016|
|Toshiba Medical Visualization Systems (UK)||2014 – 2014|
|Image Analysis Research Intern|
|NCR Corp. (UK)|
|Technology and Research Intern||2010 – 2011|
|University of Dundee (UK)||2011 – 2015|
|PhD: Tumor Localisation in Histopathology Images|
|University of Dundee (UK)||2008 – 2011|
|BSc Applied Computing (Hons)|
Here is a snapshot of some of the methods I have developed:
|Multiple Instance Learning||Transition Module|
|Technique for training neural networks with weak or coarse labels grouped together in “bags”||A method for preventing overfitting in 2D convolutional neural networks by gradually reducing dimensionality|
|Tumor Cellularity Assessment||Superpixel Classification|
|Fully automated neural network that assesses cellularity in digitized histology images for tumor burden assessment||Algorithm for classifying superpixels using features encapsulating contextual information|
My most recent publications are shown here but a full list is also available.
SPIE Medical Imaging, 8 (3), 2021.
Scientific Reports, 2019.
Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 7 , 2019.
Machine Learning for Health Workshop, NeurIPS 2018, 2018.
SPIE Medical Imaging, 2018.