Repository Index

Vision-Language Pathology Foundation Model - Nature Medicine

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

U-Net Brain Tumor Segmentation

• tensorflow
• tensorlayer
• unet
• medical-imaging

Dicoogle - Open Source PACS

• dicoogle
• dicom
• pacs
• medical-imaging

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

Sequential regulatory activity predictions with deep convolutional neural networks.

Tools for computational pathology

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

Iterative Linear Quadratic Regulator with auto-differentiatiable dynamics models

• trajectory-optimization
• differential-dynamic-programming
• theano
• dynamics-models

GEARS is a geometric deep learning model that predicts outcomes of novel multi-gene perturbations

Genomic Pretrained Network - GPN, GPN-MSA, PhyloGPN, GPN-Star

• dna
• language-model
• variant-effect-prediction
• genomics

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

The official implementation of 3D Equivariant Diffusion for Target-Aware Molecule Generation and Affinity Prediction (ICLR 2023)

• ai-for-science
• diffusion-models
• drug-design
• equivariant-networks

GraphDTA: Predicting drug-target binding affinity with graph neural networks

Source code for "DeepDTA: deep drug-target binding affinity prediction"

• protein-ligand-interactions
• convolutional-neural-networks

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

RNA-seq prediction with deep convolutional neural networks.

MolTrans: Molecular Interaction Transformer for Drug Target Interaction Prediction (Bioinformatics)

Converts medical images to more displayable formats, e.g. NIfTI to jpg.

PyTorch Connectomics: segmentation toolbox for EM connectomics

• computer-vision
• neuroscience
• connectomics
• pytorch

Medical LLM agent and MRI segmentation workspace built on PharmaLLM, with LangGraph, vLLM, and BEU-Net.

Cancer Imaging Phenomics Toolkit (CaPTk) is a software platform to perform image analysis and predictive modeling tasks. Documentation: https://cbica.github.io/CaPTk

• cancer-imaging-research
• radiomics
• radiomics-features
• machine-learning

A Network Integration Approach for Drug-Target Interaction Prediction

A C# DICOM Library

• dicom
• medical-physics
• radiology
• radiation-oncology

MOGONET (Multi-Omics Graph cOnvolutional NETworks) is a novel multi-omics data integrative analysis framework for classification tasks in biomedical applications.