A PyTorch Library for Accelerating 3D Deep Learning Research
- pytorch
- 3d-deep-learning
- neural-networks
- differentiable-rendering
68,760 visible repositories in the index
1–23 of 23 · Most stars
A PyTorch Library for Accelerating 3D Deep Learning Research
TranslatedDeepAnalyze is the first agentic LLM for autonomous data science. 🎈Your AI data analyst, automatically analyzes large amounts of data and generates professional analysis reports with one click!
3D visualization and mesh analysis for science and engineering
MNE: Magnetoencephalography (MEG) and Electroencephalography (EEG) in Python
Unsupervised Learning for Image Registration
Semi Supervised Learning for Medical Image Segmentation, a collection of literature reviews and code implementations.
Single-cell analysis in Python. Scales to >100M cells.
Sverchok is Node-based visual programming for parametric 3D modeling and computational geometry in Blender
A python module for scientific analysis of 3D data based on VTK and Numpy
Python+Numpy+OpenGL: fast, scalable and beautiful scientific visualization
Wallet UI simulation tool for BTC, ETH, SOL, XRP, and 200+ assets. Customize displayed balances in a controlled demo environment with persistent visual settings and screenshot-ready previews. Designed for testing, demonstrations, and UI prototyping without altering actual blockchain balances or transactions.
Tasks Assessing Protein Embeddings (TAPE), a set of five biologically relevant semi-supervised learning tasks spread across different domains of protein biology.
Interactive network visualization in Python and Dash, powered by Cytoscape.js
Spatial Single Cell Analysis in Python
Main yt repository
Vision-Language Pathology Foundation Model - Nature Medicine
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.
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".
A Python interface to access neuroanatomical atlases
The implementation code for "DAGAN: Deep De-Aliasing Generative Adversarial Networks for Fast Compressed Sensing MRI Reconstruction"
MCP server for spatial transcriptomics analysis through natural language interfaces.
A foundation model-driven whole slide image-scale cell phenotyping method with QuPath integration Resources
Fast, deterministic cell-type deconvolution for million-bin spatial transcriptomics