lung-cancer

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.

R package for lung-cancer risk-prediction model PLCOm2012

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

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

Hybrid 3D lung cancer nodule classifier with Hugging Face deployment

MDPD - Microbiome Database of Pulmonary Diseases

A missing-aware multimodal framework that fuses CT, Whole-Slide Histopathology, and clinical tabular data for survival prediction in unresectable stage II–III Non-Small Cell Lung Cancer (NSCLC) — without dropping patients or imputing absent modalities.