liver-cancer

Awesome artificial intelligence in cancer diagnostics and oncology

Extract and evaluate radiomics for liver cancer tumors from DICOM segmentation masks. Using SimpleITK, PyRadiomics and PyDicom.

CirrMRI600+: Large Scale MRI Collection and Segmentation of Cirrhotic Liver

Research software and privacy-safe aggregate results for multimodal classification of primary liver tumors on multiphase CT, with internal fusion evaluation and external transportability stress testing.

SimSiam self-supervised pretraining for three-class liver-tumour classification from CT images. Research snapshot · MICCAI Workshops 2023.

Official Repository for the MELBA paper entitled "Dimensionality Reduction and Nearest Neighbors for Improving Out-of-Distribution Detection in Medical Image Segmentation".

Liver cancer is one of the most dangerous diseases and is one of causes leading of death. The application of science and technology in the diagnosis and identification of cancerous tissues of the liver plays a very important role. This assists the doctor in planning and treating the patient. In this paper, we study the application of convolution…

Classification model for 1 year survival rates in patients with HCC (hepatocellular carcinoma).

Deep learning-based liver tissue image classification model that identifies 5 classes: Angiosarcoma, Cholangiocarcinoma, Healthy, Hemangioma, and Hepatocellular Carcinoma.

Data and analysis pipeline for a study on the potential advantages of daily adaptive liver SBRT performed at the University Hospital Zurich.

This repository contains the code for an AI-Based Universal Lesion Segmentation application based on my master's thesis. It includes a deep learning model for CT scan segmentation, a web interface built with Vite and React, and a Flask server for backend support.

Playground for trying to predict liver cancer

Physics-Informed & Disentangled Synthesis for Functional Liver MRI.

CancerLivER: a database of liver cancer gene expression resources and biomarkers

Kaplan-Meier survival analysis for "Living Donor Liver Transplant for Intrahepatic Cholangiocarcinoma"

Deep learning model for automatic multiclass liver tumor segmentation from CT scans using U-Net architecture.

Developed a complete data science pipeline to predict one-year survival in Hepatocellular Carcinoma (HCC) patients, achieving top performance and graded 20/20.

CEliver model

Machine learning pipeline for liver cancer classification using gene expression data. Includes preprocessing, PCA, and model evaluation with Random Forest, SVM, XGBoost, and MLP.