Popular GitHub Repositories for Kidney Histology

A Python toolkit for pathology image analysis algorithms.

A Machine Learning and Deep Learning based webapp used to predict multiple diseases.

Multiple disease prediction such as Diabetes, Heart disease, Kidney disease, Breast cancer, Liver disease, Malaria, and Pneumonia using supervised machine learning and deep learning algorithms.

Feather - Lightweight supervised slide foundation models for pathology

Federated Learning for Computational Pathology - Medical Image Analysis

Code for the im4MEC model described in the paper 'Interpretable deep learning model to predict the molecular classification of endometrial cancer from haematoxylin and eosin-stained whole-slide images: a combined analysis of the PORTEC randomised trials and clinical cohorts'.

HistoSeg is an Encoder-Decoder DCNN which utilizes the novel Quick Attention Modules and Multi Loss function to generate segmentation masks from histopathological images with greater accuracy. This repo contains the code to Test and Train the HistoSeg

Graph + Image Bimodal Deep Learning for Histology Image Classification

Synthetic histology using GANs, for deep learning model explainability and trainee education.

ECCV-AIMIA 2022 paper: "Multi-Scale Attention-based Multiple Instance Learning for Classification of Multi-Gigapixel Histology Images"

Deep Learning for Kidney Histologic Primitives (U-Net implementation in PyTorch)

Using machine learning models to predict if patients have chronic kidney disease based on a few features. The results of the models are also interpreted to make it more understandable to health practitioners.

An end to end automatic segmentation tool for MRI files, that can identify lungs, bladder, liver, kidney and bone

Repository for SegMNet project. Kidney tumor/cyst segmentator based on DeepLabV3+ (UNet++) and KiTS23 dataset.

HealthFood Recommender: Personalized food recommendations for obesity, cardiovascular disease, chronic kidney disease, diabetes, and anemia patients using machine learning.

Deep-learning based classification pipeline for subtyping lung tumors from histology. Study design and codebase to analyze the impact of nucleus segmentation on subtyping.

? AI-powered system for Chronic Kidney Disease staging and treatment. Combines a stacking ensemble (CatBoost, XGBoost, LightGBM) with clinical rules to achieve 96.8% accuracy. Includes SHAP explainability, a Streamlit web app, PDF reports, and expert-aligned treatment guidance. Built for clinicians, researchers, and ML developers in healthcare AI.

This application to detect kidney cancer.

Chronic kidney disease prediction done using Artificial Neural Network.

Kidney-Disease-Classification-Deep-Learning-Project-MLflow-DVC