In-depth motion analysis of mobile lung cancer tumors. Designed for 4D-CT scans of the thorax and provide valuable information for proton therapy treatment planning

Biomedical Image Processing involves applying computer algorithms to analyze and enhance medical images, such as X-rays or MRI scans. It aims to extract meaningful information, diagnose diseases, and aid in medical research by employing advanced image analysis techniques and computational tools.

COVID-19 CT scan image classification using EfficientNetB2 with transfer learning and deployment using Streamlit. This project focuses on accurately classifying CT scan images into three categories: COVID-19, Healthy, and Others. Leveraging transfer learning on pretrained EfficientNetB2 models, the classification model achieves robust performance.

A repository containing deep learning models and evaluation methods for enhancing medical image segmentation in Computed Tomography (CT) scans, with a focus on U-Net variants, nnUNet, and Swin-UNet architectures.

COVID-19 Detection Chest X-rays and CT scans: COVID-19 Detection based on Chest X-rays and CT Scans using four Transfer Learning algorithms: VGG16, ResNet50, InceptionV3, Xception. The models were trained for 500 epochs on around 1000 Chest X-rays and around 750 CT Scan images on Google Colab GPU. A Flask App was later developed wherein user can…

A command line tool to transform a DICOM volume into a 3d surface mesh (obj, stl or ply). Several mesh processing routines can be enabled, such as mesh reduction, smoothing or cleaning. Works on Linux, OSX and Windows.

Image-to-image deep learning framework for MRI to porosity map translation

Deep CNN-Based CAD System for COVID-19 Detection Using Multiple Lung CT Scans.

🔀 Medical software for Processing multi-Parametric images Pipelines

View volumetric (3D) medical images in Jupyter notebooks

An official implementation of PCRLv2 (pre-training and fine-tuning code are included).

Application for displaying and analyzing 3D volumes that utilizes custom made engine.

Unity3d Prototype to manipulate Hounsfield units and create a 3D render of dicom images

Segmentation and Classification models for COVID CT scans (COVID, pneumonia, normal) based on Mask R-CNN.

Train a 3D Convolutional Neural Network to detect presence of brain stroke from CT scans.

Train a 3D convolutional neural network to predict presence of pneumonia.

Adopted a convolutional neural network for COVID-19 testing. Examined the performance of different pre-trained models on CT testing and identified that larger, out-of-field datasets boost the testing power of the models.

LUng CAncer Screeningwith Multimodal Biomarkers

Machine learning models for multi-organ, multi-disease prediction in chest CT volumes. From paper Draelos et al. "Machine-Learning-Based Multiple Abnormality Prediction with Large-Scale Chest Computed Tomography Volumes."