Dr. Harry Pratt
Verified Expert in Engineering
Computer Vision Developer
Harry has an aptitude—and enjoyment—for applying the latest deep learning and machine learning methods to commercial problems. He's applied this knowledge to research during his Ph.D. in deep learning and throughout his commercial experience in the health care and mobile gaming industries. Harry has hands-on experience in developing and delivering the vast majority of machine learning models from simple logistic regression models to large-scale computer vision models.
Python 3, Keras, TensorFlow, Linux, PyCharm
The most amazing...
...project I've undertaken achieved national newspaper and TV coverage for image analysis work relating to skin cancer detection.
Machine Learning Engineer
- Increased game revenue by implementing user-level optimization of game configurations for cohorts of users through an ML-based configuration and developing an A/B test system.
- Detected upcoming concept trends in the App Store which led to a higher success rate in game development.
- Implemented real-time game categorization and similarity comparison using computer vision and NLP models.
- Optimized the accuracy of disease detection which allowed nuanced visualization of features through the extraction of model parameters. This provided clinical justification for our methods and diagnosis.
- Improved the prediction in rare disease classification by 20% through training on synthetic data augmented via the use of GANs.
- Implemented UNet segmentation and R-CNN object detection for biomarker analyses.
Ph.D. Candidate and Researcher
University of Liverpool
- Worked on deep learning techniques to tackle medical imaging problems; specifically diabetic retinopathy.
- Developed, using mainly OpenCV, a UV image analysis to determine if specific areas of the face are missed during routine sunscreen application and whether the provision of public health information is sufficient to improve coverage.
- Constructed the architecture for a Fourier Convolution Neural Network (FCNN) for medical image classification (ECML); the advantage offered is that there is a significant speedup in training time without loss of effectiveness.
- Created a convolutional neural network approach for first locating vessel junctions and then classifying them as either branchings or crossings which helped with the challenges involved in the quantitative analysis of retinal blood vessels.
- Developed a CNN approach, using digital fundus images, to diagnose and accurately classify the disease severity for diabetic retinopathy.
UV Image Analysis for Skin Cancerhttps://journals.plos.org/plosone/article?id=10.1371/journal.pone.0185297
To investigate this, 57 participants were imaged with a UV sensitive camera before and after sunscreen application: first visit; minimal pre-instruction, second visit; provided with a public health information statement. Images were scored using a custom automated image analysis process designed to identify high UV reflectance areas, i.e., missed during sunscreen application, and analyzed for 5% significance.
Object detection and image segmentation revealed eyelid and periorbital regions to be disproportionately missed during routine sunscreen application. The provision of health information caused a significant improvement in coverage to eyelid areas in general; however, the medial canthal area was still frequently missed.
Development of Novel Fourier CNN for Medical Image Classification (ECML)
Using the proposed approach larger images can therefore be processed within viable computation time. The evaluation was conducted using the benchmark Cifar10 and MNIST datasets, and a bespoke fundus retina image dataset.
The results demonstrate significant speedup without adversely affecting accuracy. This work was presented at the ECML conference in 2018.
Detection and Identification of Vessel Junctions in Fundus Photography
In this work, I presented a new technique for tackling this challenging problem by developing a convolutional neural network approach for first locating vessel junctions and classifying them as either branchings or crossings. We achieved a high accuracy of 94% for junction detection and 88% for classification.
Combined with work in segmentation, this method has the potential to facilitate automated localization of blood clots and other disease symptoms, leading to improved management of eye disease through aiding or replacing a clinician's diagnosis.
Convolutional Neural Networks for Diabetic Retinopathyhttps://www.sciencedirect.com/science/article/pii/S1877050916311929
In this work, I developed a CNN approach to diagnosis from digital fundus images that can accurately classify the disease severity. The developed model architecture alongside the image preprocessing and data augmentation can identify the intricate features involved in the classification task and their respective grading rules. The model can consequently automate the clinical screening process.
The model is trained using a GPU on a large publicly available dataset and demonstrates impressive results, particularly for a high-level multi-class classification task; it achieves a Kappa score of 0.75.
Mobile App Classification and Similarity Scoring
This clustered all scraped games allowing us to track growing clusters and produce game similarity scores. The real-time calculation was highly optimizing using tensor operations in TensorFlow as there were thousands of new games scraped a day and hundreds of thousands of games to compare to in the resulting database.
Regardless, the calculations of the model and similarity check was able to be performed in milliseconds per game.
User Segmentation for Personalized Game Mechanics
This allowed personalized configurations for the user cohorts which optimized the amount of time they interacted with the game and their potential revenue.
Development of a Machine Learning A/B Test System
U-Net Feature Segmentation for High Resolution Medical Imaging
High dice coefficient and IoU metrics were achieved which generalized well across multiple ethnicities, nationalities, and camera types.
Biomarker Detection and Segmentation in Medical Imaging
Image Augmentation and Classification for Rare Disease Using GANs
Therefore, this work utilized GANs in order to learn the latent space of rare diseases and produce more synthetic data from the learned latent space. These images were then used to improve the accuracy of the rare disease classification by an additional 20%.
Keras, TensorFlow, OpenCV, Pandas, Theano, Scikit-learn
Computer Vision, Deep Learning, Deep Neural Networks, Convolutional Neural Networks, Machine Learning, Image Analysis, Image Classification, Applied Mathematics, Statistics, Data Analysis, Object Detection, Natural Language Processing (NLP), Clustering, Logistic Regression, Metaflow, Image Segmentation, Generative Adversarial Networks (GANs), Object Tracking, Image Recognition, GPT, Generative Pre-trained Transformers (GPT)
Ph.D. in Deep Learning for Medical Imaging
University of Liverpool - Liverpool, England
Master's Degree in Mathematics
University of Liverpool - Liverpool, England
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