Matlab GUI code to read and analyze CY Scan images in DICOM format

VasculAR - Integration of Deep Learning into automatic volumetric cardiovascular dissection and reconstruction in simulated 3D space for medical practice

Machine Learning for COVID-19 Data Analysis Project

Msc Thesis notes - Evaluation of the effectiveness of artificial neural networks in reducing noise in chest images obtained by various computer tomography methods

The study works on generating CT images from MRI images, where unsupervised learning was used using VAE-CycleGan. Since the number of samples included in the data set used in the study, and therefore in this case we are in a state of epistemic uncertainty, therefore probabilistic models were used in forming the latent space.

Standard Phantom for Medical 3D printing modeling software evaluation

Series of code files related to surface roughness chracterisation using surface generation on ImageJ, CT scans and machine learning.

3D Segmentation of Lungs on CT

LUNA(LUng Nodule Analysis) 2016 Segmentation Pipeline

CT Intensity Segmentation of Lungs

Detecting Laryngeal Cancer from CT SCAN images using Improvised Deep Learning based Mask R-CNN Model

Automatically convert 2D medical images (DICOM) to 3D using VTK and python

CNN architectures Resnet-50 and InceptionV3 have been used to detect whether the CT scan images is covid affected or not and prediction is validated using explainable AI frameworks LIME and GradCAM.

I will use the CT Scan of the brain image dataset to train the CNN Model to predict the Alzheimer Disease.

Code for doing binary image classification using Keras in R.

curriculum development ideas for computational biology internship and teaching assistantship @ AI4ALL

Fully automated code for Covid-19 detection from CT scans from paper: https://doi.org/10.1016/j.bspc.2021.102588

An Ensemble Transfer Learning Network for COVID-19 detection from lung CT-scan images.

Developing a well-documented repository for the Lung Nodule Detection task on the Luna16 dataset. This work is inspired by the ideas of the first-placed team at DSB2017, "grt123".