Large Covid-19 CT scans dataset from the paper: https://doi.org/10.1016/j.bspc.2021.102588

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".

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

LUNA(LUng Nodule Analysis) 2016 Segmentation Pipeline

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

A PyTorch implementation of image segmentation GAN from the paper "SegAN: Adversarial Network with Multi-scale L1 Loss for Medical Image Segmentation".

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

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.

A rule-based algorithm enabled the automatic extraction of disease labels from tens of thousands of radiology reports. These weak labels were used to create deep learning models to classify multiple diseases for three different organ systems in body CT.

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

Deep-Learning solution for detecting Intra-Cranial Hemorrhage (ICH) 🧠 using X-Ray Scans in DICOM (.dcm) format.

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

3D Segmentation of Lungs on CT

A series of networks to implement photorealistic renderings through the MeVis Path Tracer from: "The SmARTR Pipeline: a modular workflow for the cinematic rendering of 3D scientific imaging data"

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

A simple code useful for covid-19 detection on CT Scans

A Deep Learning-based Web App that classifies kidney CT scan images into 4 categories using Vision Transformers (ViT) and Transfer Learning. The backend is built with Flask, and the frontend is designed using HTML, CSS, and JavaScript.

Detecting tumors in CT scan images using GLCM matrix

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