FMRI is a technology used to measure brain activity, typically while the subject is performing a task in the MRI scanner. It works by measuring how blood flow changes in response to neuronal activity. The resulting data are a time series of brain volumes. After pre-processing, we can compute fMRI brain maps which roughly reflect locations of brain activity corresponding to the task the subject was involved in i.e. a single map for each subject/activity. One important topic in neuroscience is to assess to what extent one can predict behavior from fMRI brain maps. This is known as “decoding” or “brain reading” [2, 3]. Each brain image may be associated with multiple cognitive processes (sub-tasks). Ideally, decoding can predict which sub-tasks are associated with each brain map [1] -- denoted as a set of labels.

The prediction problem which involves predicting a set of labels is known as multilabel classification. It is often thought of as the simplest kind of structured prediction [4, 5].

Your goal is to predict a set of labels associated with each fMRI brain image. You can use any technique you like. Your project will be judged based on performance on two metrics: Subset Accuracy and Micro-averaged AUC.

[1] Schwartz, Yannick, Bertrand Thirion, and Gaël Varoquaux. "Mapping paradigm ontologies to and from the brain." Advances in neural information processing systems. 2013.

[2] Varoquaux, Gael, and Bertrand Thirion. "How machine learning is shaping cognitive neuroimaging." GigaScience 3.1 (2014): 28.

[3] Pereira, Francisco, Tom Mitchell, and Matthew Botvinick. "Machine learning classifiers and fMRI: a tutorial overview." Neuroimage 45.1 (2009): S199-S209.

[4] Tsoumakas, Grigorios, and Ioannis Katakis. "Multi-label classification: An overview." International Journal of Data Warehousing and Mining 3.3 (2006).

[5] Koyejo, Oluwasanmi O., et al. "Consistent multilabel classification." Advances in Neural Information Processing Systems. 2015.

• Real-world experience on general ML principles, multilabel classification, blind test evaluation
• Investigate the importance of metrics on classification (the best answer for each of the metrics will differ)
• Investigate the effect of spatially correlated features on classification. Use of convolution and other image processing techniques may be helpful.
• Investigate learning with high dimensional input features
• Investigate data preprocessing/cleaning.

• Label names: tag_name.npy : identifies each label, not required for your model
• Training features: train_X.npy (4-D tensor. first dimension is N = number of examples). Each sample is a low resolution brain image representing brain activity corresponding to the given labels.
• Training labels: train_binary_Y.npy: labels represented as binary vector, Size = number of examples * number of labels.
• Test features: valid_test_X.npy

Test labels are submitted as a text file to the kaggle site. Please use the provided python script to convert your binary matrix prediction into a format that Kaggle will accept. The order of your predictions should line up with the order of examples in valid_test_X.npy Usage: python3 npy_to_csv.py [subset|auc] <prediction_file> <output_file> e.g. python3 npy_to_csv.py subset result.npy subset_submission.csv e.g. python3 npy_to_csv.py auc result.npy auc_submission.csv

• Subset Accuracy Competition: The evaluation metric is identical to a. converting from multilabel to multiclass i.e. a different class for each label configuration b. Applying this metric to the resulting multiclass labels
• Micro-averaged AUC Competition: The evaluation metric is identical to #sklearn.metrics.roc_auc_score (with average = “micro”).