ನಮಸ್ಕಾರ, नमस्ते, Hi there,
I’m a graduate student in the School of Computing Science at the Simon Fraser University (SFU), Burnaby. I work as a Research Assistant at the Medical Image Analysis Lab of SFU under the supervision of Prof. Ghassan Hamarneh. My research interests lie in the application of Computer Vision and Deep Learning for Medical Image Analysis.
I started my Masters (Thesis-based M.Sc.) in the Fall of 2019. Before joining SFU, 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: mmallya
atsfudotca
Publications
2. Deep Multimodal Guidance for Medical Image Classification
M Mallya, G Hamarneh
MICCAI 2022 (PDF, CODE, VIDEO, POSTER)
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.
Relevant projects
3. Brain Tumor Classification using Graph Convolution Networks
M Mallya
Project done as part of Deep Learning for Graphs course in Summer 2020 (PDF, CODE)
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
M Mallya
Project done as part of the Mitacs Globalink research internship in Summer 2017 (PDF)
Relevant coursework
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)
Organizations
Last updated: 13 May 2022