Unsupervised pattern recognition in biomedical image data
Reza Abbasi-Asl, Professor
Neuroscience
Applications for Fall 2024 are closed for this project.
Computational tools based on machine learning principles have shown promising results in analyzing medical images. Deep convolutional neural networks (CNNs) are one of the most successful tools in this domain. While most of the algorithms based on CNNs are supervised and with the increasing amount of un-labeled datasets, it is essential to design unsupervised or semi-supervised methods for analyzing medical images. In this project, we study unsupervised and semi-supervised methods to analyze two types of biomedical images from brain: 1. MR and fMRI images from humans and 2. Gene expression images in brain. We use tools such as deep autoencoders and non-negative matrix factorization (NMF) to find meaningful patterns in these images. The MRI dataset is from thousands of patients at UCSF as well as large-scale imaging datasets available online. The gene expression data is collected in collaboration with Allen Institute. This study could have valuable impact on relating patient health data to observed symptoms as well as help clinicians to easily benefit from the information hidden in the data.
Role: The successful candidate will start by reading relevant papers and learning about the dataset. Then the candidate will implement tools based on deep autoencoders, NMF, and semi-supervised deep convolutional neural networks to analyze the data. The candidate will spend time on assessment of the patterns in the images identified through the algorithms and synthesize the results. Finally, the results will be documented and presented in form of a research publication. Note that the successful candidate will be working remotely on this project.
Qualifications: Candidate should have experience programming with Python and implementation of neural networks in platforms such as PyTorch, TensorFlow, Keras, or Caffe. Familiarity with convolutional neural networks, image processing, and medical image analysis is desirable but not essential.
Hours: 12 or more hours
Off-Campus Research Site: remote
Related website: http://abbasilab.org
Digital Humanities and Data Science Biological & Health Sciences