Awesome artificial intelligence in cancer diagnostics and oncology

AiAi.care project is teaching computers to "see" chest X-rays and interpret them how a human Radiologist would. We are using 700,000 Chest X-Rays + Deep Learning to build an FDA 💊 approved, open-source screening tool for Tuberculosis and Lung Cancer. After an MRMC clinical trial, AiAi CAD will be distributed for free to emerging nations, charita…

This is the segmentation process of the LIDC-IDRI dataset. Checkout my preprocessing repository to use this repository

[PR2022] The Code of “EANet: Iterative Edge Attention Network for Medical Image Segmentation”

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

This is a WebApp, which detects lung diseases with integrated stripe payment processing.

Sequence-to-sequence image and contour prediction library for longitudinal datasets [PMB'23]

Code for preprocessing the LIDC-IDRI lung cancer screening CT scan dataset

A Machine Learning Project to detect if someone has Lung Cancer or not. Built with FastAPI, Streamlit and Docker.

Deep features and radiomics selection with NSGA-II for pulmonary nodule classification

A module for the Insight Toolkit (ITK) that provides a generic, modular, and extensible architecture for lesion sizing algorithms in medical images as well as a reference algorithm for lung solid lesion segmentation in CT images.

Optimized pipelines for Spatial Transcriptomics (ST) data analysis using Seurat & Giotto, designed for reproducible benchmarking and biological insight.

Code for our BVM workshop submission "Attention-based Multiple Instance Learning for Survival Prediction on Lung Cancer Tissue Microarrays"

R package for lung-cancer risk-prediction model PLCOm2012

Final year Btech Lung-Cancer-Detection-Project with code and documents. Project Includes Source Code, PPT, Synopsis, Report, Documents, Base Research Paper & Video tutorials

Exploratory Data Analysis of Lung Cancer Risk Factors

This project predicts lung cancer risks using machine learning models like Random Forest, Logistic Regression, and SVM. It analyzes patient data with features such as age, smoking habits, and symptoms. Data preprocessing, visualization, and performance evaluation ensure accurate predictions for early diagnosis.

Find patients who have concerning tests but no timely follow-up