Deep Learning in Medical Imaging

BIDS and the UCSF Department of Radiology and Biomedical Imaging are excited to offer a combined educational and research opportunity for motivated undergraduate students in the medical imaging research team. Eligible undergraduates may apply online August 19-31, 2020.

Towards Data Driven Medicine: Advances in artificial intelligence have the potential in transforming the field of medicine. Medical diagnostics and treatments are fundamentally a data problem. Turning medical images, lab tests, genomics, patient histories into accessible, clinically-relevant insights requires new collaborations between the traditional domains of biomedical research and data science specialties like machine learning. Our laboratory at UCSF specializes in acquisition, reconstruction, post-processing, and quantitative analysis of Magnetic Resonance (MR) brain images. The wealth of the imaging data collected in our laboratory over the years has not been utilized to its full potential. Working in close collaboration with Berkeley Institute for Data Science we would like to develop methods, tools and pipelines to fully utilize our imaging data to help clinicians make better decisions about treatment strategies for patients with brain tumors using deep learning approaches.

Deep learning is a new and powerful machine learning method, which utilizes a range of neural network architectures to perform several imaging tasks, which up to now have included segmentation, object (i.e. lesion or region of interest) detection and classification. Deep learning methods are different from the conventional machine learning methods (i.e. support vector machine (SVM) and random forest (RF)) in one major sense: the latter rely on feature extraction methods to train the algorithm, whereas deep learning methods learn the image data directly without a need for feature extraction.

Multiple opportunities for projects that capture and extract information from our imaging data are available. Our projects are centered around a very popular deep learning method in medical imaging field which is convolutional neural networks (CNN). We will be using the NiftyNet platform ( to build new solutions to our various imaging problems. The possible projects include brain tumor segmentation, image reconstruction, image synthesis, etc. The details for each project will be discussed during the interview process.

Students from various majors are encouraged to apply, including but not limited to EECS, BioE, CS, data science, math, and statistics. They will work in teams and closely with graduate students, post-docs and data scientist mentors.


URAP students will be tasked with developing, implementing, refining, and testing algorithms and workflows to achieve the specific goal in their chosen project. The specific selection of available tasks will depend on the chosen project and the progress in the time line of the project, and the student's experience and preferences, but the common tasks are:

  • Attend the group meeting in BIDS most likely on Wednesdays (exact time will be announced later)
  • Attend individual meeting with the supervisor once at the beginning, in the middle and at the end of the first and second semester.
  • Lead 1-3 weekly seminars/hackathons on deep learning research paper and coding discussions per semester.
  • Give a formal presentation at the end of each semester to BIDS and UCSF community
  • Upon successful progress, it is expected that students submit/present at a national research meeting. Students are encouraged to seek out and apply for undergraduate research grants.

Required Qualifications

  • Proficiency in programming languages (Python and/or MATLAB and/or R preferred)
  • Familiarity with the Linux/Unix environment
  • Working knowledge of Version Control (such as Github).
  • Great teamwork (organization, communication skills, punctuality, reliability, etc)
  • Interest in data science, medical imaging, machine learning, engineering and healthcare research

Recommended Qualifications

  • Working knowledge of basic machine learning and deep learning (cost function, cross-validation, overfitting, error analysis, etc)
  • Working knowledge of Tensorflow/Keras or Pytorch
  • Working knowledge of signal processing & image processing

Offered through UC Berkeley's Undergraduate Research Apprentice Program (URAP) for the Spring 2020 and Fall 2020 academic semesters. 

BIDS Affiliates

Maryam Vareth

Health and Life Sciences Lead