This exercise has been modified from the original example in order to be able to integrate it into course/s at higher education institutions.
It shows how to work with an MRI brain image dataset and how to use transfer learning to modify and retrain ResNet-18, a pretrained convolutional neural network, to perform image classification on that dataset. The MRI scans used in this example were obtained during a study [1] of social brain development conducted by researchers at the Massachusetts Institute of Technology (MIT), and are available for download via the OpenNEURO platform: https://openneuro.org/datasets/ds000228/versions/1.1.0
- Teaching end-to-end AI workflow for image classification
- Applying transfer learning to a real-world data
- Modifying a pre-trained network interactively and programmatically
- Training the modified network to classify MRI images
- Evaluating the model
- Understanding network predictions using occlusion sensitivity maps
Instructors teaching any course in neuroscience and/or bio-medical disciplines which uses MRI data. The exercises can be used with students who have very little programming knowledge (use DND version) or with students who are familiar with programming (use non-DND version).
The exercise can be run within 30-45 minutes.
The package contains an interactive, app-based exercise (using Deep Network Designer=DND). it consist of a working exercise where parts of the code have to be filled by students. The exercise is self-explanatory and contain all information required to execute it. the app-based workflowis suitable even if students have very little programming knowledge. The programmatic workflow is also included for ease of understanding.
The exercise can be used as part of a course/module/lecture or as self-directed and self-paced exercise/homework/assignment.
The package contains a small subset of the original dataset. Using the original dataset (20+GB) would result in longer run times.
The solution files are available upon instructor request. If you would like to request solutions or have a question, contact the MathWorks online teaching team.
R2024b
[1] Richardson, H., Lisandrelli, G., Riobueno-Naylor, A., & Saxe, R. (2018). Development of the social brain from age three to twelve years. Nature Communications, 9(1), 1027. https://doi.org/10.1038/s41467-018-03399-2
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