Repository Index

AI Toolkit for Healthcare Imaging

• healthcare-imaging
• deep-learning
• medical-image-computing
• medical-image-processing

OHIF zero-footprint DICOM viewer and oncology specific Lesion Tracker, plus shared extension packages

• nci-itcr
• nci-qin
• quantitative-imaging
• imaging-informatics

This repository includes the official project of TransUNet, presented in our paper: TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation.

Multi-platform, free open source software for visualization and image computing.

• medical-imaging
• vtk
• itk
• python

Medical imaging processing for AI applications.

• pytorch
• medical-image-computing
• deep-learning
• data-augmentation

Many studies have shown that the performance on deep learning is significantly affected by volume of training data. The MedicalNet project provides a series of 3D-ResNet pre-trained models and relative code.

Pytorch 3D U-Net Convolution Neural Network (CNN) designed for medical image segmentation

Large Language-and-Vision Assistant for Biomedicine, built towards multimodal GPT-4 level capabilities.

Use of Attention Gates in a Convolutional Neural Network / Medical Image Classification and Segmentation

• convolutional-neural-networks
• attention-model
• image-classification
• image-segmentation

A pytorch-based deep learning framework for multi-modal 2D/3D medical image segmentation

• segmentation
• deep-learning
• pytorch
• medical-imaging

Train AI models efficiently on medical images using any framework

• pytorch
• deep-learning
• keras
• neuroimaging

Insight Toolkit (ITK) -- Official Repository. ITK builds on a proven, spatially-oriented architecture for processing, segmentation, and registration of scientific images in two, three, or more dimensions.

• itk
• insight-toolkit
• image-analysis
• medical-imaging

TorchXRayVision: A library of chest X-ray datasets and models. Classifiers, segmentation, and autoencoders.

• medical-imaging
• medical-ai
• deep-learning
• machine-learning

⚡High Performance DICOM Medical Image Parser in Go.

• dicom
• dicom-images
• golang
• golang-library

3D medical imaging reconstruction software

• dicom
• dicom-viewer
• magnetic-resonance-imaging
• medical-image-processing

Simultaneous Nuclear Instance Segmentation and Classification in H&E Histology Images.

Vision-Language Pathology Foundation Model - Nature Medicine

• bioimage-analysis
• bioimage-informatics
• conch
• foundation-model

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.

• histopathology-images
• microscopy
• sliding-windows
• medical-image-analysis

Tools for computational pathology

• machine-learning
• digital-pathology
• computational-pathology
• biomedical-image-processing

Pathology Language and Image Pre-Training (PLIP) is the first vision and language foundation model for Pathology AI (Nature Medicine). PLIP is a large-scale pre-trained model that can be used to extract visual and language features from pathology images and text description. The model is a fine-tuned version of the original CLIP model.

• artificial-intelligence
• clip
• pathology
• vision-and-language

Neural networks toolbox focused on medical image analysis

• python
• keras
• convolutional-neural-networks
• brain

Kaapana is an open source toolkit for state of the art platform provisioning in the field of medical data analysis. The applications comprise AI-based workflows and federated learning scenarios with a focus on radiological and radiotherapeutic imaging. The name Kaapana comes from the Hawaiian word kaʻāpana, meaning "distributor" or "part".

• imaging
• ai
• cloud-computing
• medical-imaging

Pan-Cancer Integrative Histology-Genomic Analysis via Multimodal Deep Learning - Cancer Cell

• histology
• wsi-images
• pan-cancer-analysis
• genomics

Automated 3D cell detection in very large images

• microscopy
• cell-detection
• deep-learning
• image-analysis