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

Automatic end-to-end lung tumor segmentation from CT images.

CT Scan utilities. Works with DICOM files.

This repository contains a Deep Learning model using Convolutional Neural Networks (CNN) for predicting strokes from CT scans. The model aims to assist in early detection and intervention of strokes, potentially saving lives and improving patient outcomes.

"Covid19-Detector" is a Django-ReactJS Web App with an Artificial Intelligence. It can detect COVID-19 from CT Scan Images using CNN based on DenseNet121 architecture.

CT scan machine learning models including AxialNet and HiResCAM

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"

Brain stroke detector from CT scans

"Covid19-Detector" is a Django-ReactJS Web App with an Artificial Intelligence. It can detect COVID-19 from CT Scan Images using CNN based on DenseNet121 architecture

Rede Neural Convolucional para diagnóstico de COVID-19 a partir de imagens de Tomografia Computadorizada desenvolvida para a disciplina de Diagnóstico por Imagem do curso de Informática Biomédica da UFCSPA.

Implementation of EfficientNet, MixNet, and TResNet families for COVID-19 Computed Tomography Scans Identification. This implements to our paper "Automatic detection of Covid-19 from chest X-ray and lung computed tomography images using deep neural networks and transfer learning" published on ASOC

Source code from the conference paper "Consistency of Fan-beam Projections of a Translating Object Along an Arc of a Circle"

Build 3D CNN to predict the presence of viral pneumonia in CT scans

Docker container for Landmark-free Morphometry code from A landmark-free morphometrics pipeline for high-resolution phenotyping: application to a mouse model of Down syndrome paper

CT Scan Analyzer for Rocket Engine

In many cases that doctors face during the different treatment processes for many patients, so that the doctor tries every time to remember an old pathological condition that he encountered in advance in order to try to retrieve the methodology that he followed at that moment to treat the patient who has the same pathological condition.