The work I have done while doing my Internship at METU CENG ImageLab
The work I have done was trying different models for cognitive state classification and analyzing the Functional Magnetic Resonance Imaging (fMRI) time series data recorded in three dimensional voxel coordinates by clustering the Blood Oxygen Level Dependent (BOLD) responses. The experimental results show that, on a multiclass fMRI dataset, the Deep Learning models improves the classification accuracy (an average 12.5% increase when using Convolutional Neural Networks and an average 14.43% increase when using Multilayer Perceptrons) compared to a classical multi voxel pattern analysis (MVPA) method.
** The baseline accuracy of the MVPA 33.73% on average. It is taken from MSc Thesis of Gunes Sucu
The main dataset we used in this work is a neuroimage (or image) dataset collected from 12 human subjects. For each subject, there are 210 training and 210 testing images. Each image in the dataset belongs to one of four classes.
The dataset was originally collected for an emotional memory retrieval experiment* where the stimuli consisted of two neutral (kitchen utensils and furniture) and two emotional (fear and disgust) categories of images. Each trial started with a 12-second fixation period followed by a 6-second encoding period. In the encoding period, participants were presented with 5 images from the same category, each image lasting 1200 milliseconds on the screen. Following the fifth image, a 12-second delay period was presented in which participants solved three math problems consisting of addition or subtraction of two randomly selected two-digit numbers. Following the third math problem, a 2-second retrieval period started in which participants were presented with a test image from the same category and indicated whether the image was a member of the current study list or not. The reader is referred to paper* for further details. For neuroimage classification, we employed measurements obtained during the encoding and retrieval phases as our training and testing data, respectively.
*Relationship between emotion and forgetting.
I have used two different Deep Learning models which are Convolutional Neural Networks and Multilayer Perceptrons on raw data with using some techniques to augment the data.
In all models, I used Dropout functions and Batch Normalization layers in order to prevent the overfitting problem. Since the dataset is high-dimensional (137502 features for MLP and 48*48*48*6 dimensional 3D images for CNN) and has few samples for training (210 samples for training), models tend to overfit, and that leads to poor predictive performance for models.
All the training was done on the GPU(GTX 1080 or Tesla K40c).
Code for CNN model PyTorch implementation
Code for CNN model Keras implementation
Code for MLP model PyTorch implementation
Code for MLP model Keras implementation
Also, I have done some experiments with using region-wise data which is formed by taking the average of voxels with respect to anatomic brain regions. After taking the average, the data is the dimension of 90 instead of 137502 (I used all anatomical regions defined by Anatomical Atlas Labeling (AAL)* ).
Code for Regionwise data MLP model PyTorch implementation
| Subjects | TEST ACCURACY FOR MLP WITHOUT AUGMENTATION | TEST ACCURACY FOR MLP WITH AUGMENTATION |
|---|---|---|
| s1 | 41,1 | 43,1 |
| s2 | 46,1 | 50,2 |
| s3 | 40,2 | 44,8 |
| s4 | 40,9 | 44,8 |
| s5 | 40,8 | 41,1 |
| s6 | 44,2 | 49,1 |
| s7 | 63 | 61 |
| s8 | 51,9 | 53,8 |
| s9 | 49,8 | 53,9 |
| s10 | 50,2 | 47,2 |
| s11 | 40,7 | 47,9 |
| s12 | 44,9 | 41,2 |
| s13 | 47,2 | 48,1 |
| AVERAGES | 46,23 | 48,16 |
I have also tried Autoencoders in order to decrease the number of features. Code for Autoencoders