Code for the Nature Scientific Reports paper "Pathologist-level classification of histologic patterns on resected lung adenocarcinoma slides with deep neural networks." A sliding window framework for classification of high resolution whole-slide images, often microscopy or histopathology images.

This is the preprocessing step of the LIDC-IDRI dataset

There are some examples of 3D Medical Image Process

[MICCAI 2022 Best Paper Finalist] Bayesian Pseudo Labels: Expectation Maximization for Robust and Efficient Semi Supervised Segmentation

Automatic classification of tertiary bronchi based on bronchus masks using a rule-based approach.

A basic framework for pulmonary nodule detection and characterization in CT

Lobe Segmentation

Pulmonary nodules computer-aided diagnosis demo system.

iterative Motion Compensation (iMoCo) reconstruction for MRI. Includes both Matlab and Python implementations. reference doi 10.1002/mrm.27998

Lung Extraction from Chest X-ray for Efficient Computing

Code for COVID19 CT labeling. Submillimetric CT dataset provided as well.

Lung cancer screening radiomics

Reimplementation of the PLS-Net architecture used for lung lobe segmentation in CT

Um projeto Brasileiro para combate ao COVID19.

The model is based on a YOLOv8 (Deep learning Neural network architecture) and is trained on the publicly available dataset, which consists of lung CT scans of patients with and without lung cancer.

Early exploration of detecting signs of lung pathology in COVID-19 Lung CT cases

Workflow-centred open-source fully automated lung volumetry in chest CT.

In this competition, we’ll predict a patient’s severity of decline in lung function based on a CT scan of their lungs. We’ll determine lung function based on output from a spirometer, which measures the volume of air inhaled and exhaled.

Trained a U-NET model to segment lung images. Currently working on a MultiFrequency UNet using FB2DEWT components

Final year Btech Lung-Cancer-Detection-Project with code and documents