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

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

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

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.

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

Um projeto Brasileiro para combate ao COVID19.

Lung cancer screening radiomics

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.

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.

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.

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