ನಮಸ್ಕಾರ, नमस्ते, Hi there,
I am a PhD student at the AI in Medicine Lab at the University of British Columbia (UBC) in Vancouver. I am broadly interested in the application of AI for healthcare domain. My experience lies at the intersection of Machine Learning, Computer Vision, and Medical Image Analysis.
Recently, I graduated from the School of Computing Science at the Simon Fraser University (SFU), Burnaby with an MSc degree (thesis track). During my time at SFU, I worked as a Research Assistant at the Medical Image Analysis Lab of SFU. If you are interested in my MSc research, I am attaching the link to my MSc thesis where you can find the complete information (here).
Before starting my MSc program, I did my undergrad at the National Institute of Technology Karnataka in Electronics and Communication Engineering. I was fortunate to have interned at the University of Regina and Manipal Dot Net Pvt. Ltd. during this period.
- CV: link
- Email: mayur
Leveraging the superior modality – that is usually harder to acquire – to guide the classification of the easily acquired modality using multimodal and student-teacher learning strategies. We are demonstrating the method on two datasets- RadPath (paired radiology and histopathology images) and Derm7pt (paired clinical and dermoscopic images).
1. Artifical Intelligence in Glioma Imaging: Challenges and Advances
W Jin, M Fatehi, K Abhishek, M Mallya, B Toyota, G Hamarneh
Journal of Neural Engineering, 2020 (PDF)
A review of the recent advances in the application of deep learning for brain tumor (glioma) analysis. The paper discusses the challenges involved in the process starting from data acquisition to the clinical deployment of the trained model. We further present the technical approaches currently used to overcome the discussed challenges.
2. Child-face Prediction using Generative Adversarial Networks
K Chakola, K Desai, M Mallya, V Das
Project done as part of Machine Learning course in Fall 2019 (PDF)
1. Weather Forecasting using Neural Networks
Project done as part of the Mitacs Globalink research internship in Summer 2017 (PDF)
3. Computer Vision
Implemented classical computer vision concepts and algorithms like Hough transforms, Epipolar geometry, Planar homographies, etc. as part of the Computer Vision course in Spring 2020 (CODE)
2. Deep Learning
Implemented basic neural networks for classification, CNNs for image classification, LSTMs for sentence generation, and VAEs for image generation, as part of the Deep Learning course in Spring 2020 (CODE)
1. Machine Learning
Implemented some basic ML and DL algorithms such as Linear and Logistic regression, and Transfer Learning, as part of the Machine Learning course in Fall 2019 (CODE)
Last updated: 06 Jun 2023