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k-space weighting and masking for denoising of MRI image without blurring or losing contrast, as well as for brightening of the objects in the image with simultaneous noise reduction (on the example of Agilent FID data). (Python 3)
Calculating theoretical MRI images with both TI (T1-weighting) and TE (T2-weighting) of choice, from separate T1-weighted and T2-weighted sets of images. (Python 3)
Tool for calculating swelling tablet eroding front's diffusion rate D and the rate of the swelling k from time series of either T2-maps or MRI images in FDF or Text Image format. (Python 3)
Detection and removal of biases from brain MRI using adversarial architectures. This was my final project for CS 231n (Convolutional Neural Networks for Visual Recognition) at Stanford.
k-space based details/edges detection in MRI images with optional k-space based denoising and detail control (on the example of Agilent FID data). (Python 3)
Fully supervised, healthy/malignant prostate detection in multi-parametric MRI (T2W, DWI, ADC), using a modified 2D RetinaNet model for medical object detection, built upon a shallow SEResNet backbone.
"Octopus Realtime Encephalography Lab" is the (hard) real-time networked EEG-lab framework I have developed during my PhD Thesis at Brain Research Lab of Hacettepe University Faculty of Medicine Biophysics Lab. It is meant to be a holistic golden-standard solution for all tasks of cortical source localization/networking, brain-computer interface…