ct-scan

Deep Multiple Instance Learning library for Pytorch

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.

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"

"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

Brain stroke detector from CT scans

VTK + PyQt5 volume rendering of a CT head scan and a turbulent jet simulation

"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

Hybrid CNN-Transformer architecture with Mixture of Experts for multi-organ medical image segmentation in CT scans

A deep learning system for classifying lung cancer from CT scans using CNN and Xception transfer learning. 📊 Clones dataset from GitHub, preprocesses images, trains a 4-class model (normal, adenocarcinoma, large cell, squamous cell), and predicts with visualizations. 🚀

Lightweight 3D Medical Image Segmentation pipeline optimized for low-VRAM GPUs (<8GB). Uses a Multi-Decoder U-Net to segment pelvic organs from CT data.

Production-grade Medical Single Image Super-Resolution in pure PyTorch

End-to-end deep learning system for kidney CT scan classification (Normal/Cyst/Tumor/Stone) with EfficientNetB4, Grad-CAM explainability, FastAPI backend, Streamlit UI, and complete MLOps pipeline using MLflow, DVC, Docker, and GitHub Actions.

Deep learning pipeline for lung cancer detection (YOLOv8, mAP@0.5 = 0.75) and NSCLC vs SCLC classification (ResNet18) on CT scans from the public Lung-PET-CT-Dx dataset. Final degree project, Biomedical Engineering (Universidad de Málaga).

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

An end-to-end deep learning and MLOps pipeline to classify kidney diseases from CT scan images. Built with TensorFlow and transfer learning (VGG16), featuring reproducible data versioning via DVC, experiment tracking, and an interactive Flask web application.

Neural segmentation pipeline for Intracranial Hemorrhage (ICH) detection from CT scans using ResUNet | PyTorch | Dice: 0.913