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Complete Claude Code configuration collection - agents, skills, hooks, commands, rules, MCPs. Battle-tested configs from an Anthropic hackathon winner.
Skeleton Recall Loss for Connectivity Conserving and Resource Efficient Segmentation of Thin Tubular Structures
Python library for creating flow networks and computing the maxflow/mincut (aka graph-cuts for Python)
rCM: SOTA JVP-Based Diffusion Distillation & Few-Step Video Generation & Scaling Up sCM/MeanFlow
Graph-based foundation model for spatial transcriptomics data. Zero-shot spatial domain inference, batch-effect correction, and many other features.
Rust extension module for FIJI-like colormapping and channel blending
Random k Conditional Nearest Neighbor (RkCNN) is an ensemble method aggregating k Conditional Nearest Neighbor (kCNN)
A list of summer schools on Artificial Intelligence, Machine Learning, and Healthcare
Scientific Dataset Quality Control and Data Exploration Tool
The repository provides code for running inference and finetuning with the Meta Segment Anything Model 3 (SAM 3), links for downloading the trained model checkpoints, and example notebooks that sho…
ImageJ/Fiji macros using OMERO
A library to model multivariate data using copulas.
Simple tool for clustering spatial points with categorical labels.
scPortrait is a scalable toolkit to generate single-cell representations from raw microscopy images
Implementation of multiplex Leiden for analysis of spatial omics data
The Python Risk Identification Tool for generative AI (PyRIT) is an open source framework built to empower security professionals and engineers to proactively identify risks in generative AI systems.
Differential spatial enrichment across conditions
dartR - new version - seperating dartR into specialised packages
Microsnoop: A generalist tool for microscopy image representation
Code for "MatchAnything: Universal Cross-Modality Image Matching with Large-Scale Pre-Training", Arxiv 2025.
Segment Anything for Microscopy
From Explainable to Explained AI: Ideas for Falsifying and Quantifying Explanations
Reference PyTorch implementation and models for DINOv3