Showing 1-20 of 32 projects
A comprehensive collection of deep learning, reinforcement learning, and machine learning resources for vibe coders.
A zero-footprint DICOM viewer and oncology-specific Lesion Tracker with shared extension packages.
A comprehensive index of medical imaging datasets for researchers and developers working in the medical imaging field.
A comprehensive collection of interview questions and answers for AI, computer vision, and LLM topics.
Official implementation of the UNet++ medical image segmentation model, useful for computer vision projects.
An awesome curated list of medical-related AI/ML resources including LLMs, datasets, and benchmarks.
A collection of medical imaging datasets for researchers and developers in the healthcare industry.
TorchIO is a Python library for efficient medical image preprocessing and data augmentation for AI applications.
A curated list of resources for using Generative Adversarial Networks (GANs) in medical imaging applications.
A PyTorch-based deep learning framework for 2D/3D medical image segmentation.
Efficient AI model training on medical images using any deep learning framework.
Open-source zero-footprint medical image viewer library built with JavaScript.
This GitHub repository contains deep learning papers and resources focused on medical image analysis.
A C++ library for image analysis and processing in medical imaging.
A large-scale dataset of raw MRI measurements and clinical MRI images for medical imaging research.
A curated collection of resources related to image registration, including books, papers, videos, and toolboxes.
A deep learning toolkit for medical image analysis, supporting a variety of neural network models and medical imaging tasks.
An open-source Python package for extracting radiomics features from 2D and 3D medical images and binary masks.
An open-source platform for medical image analysis and image-guided therapy research using deep learning.
A Python library for using diffusion models to segment and reconstruct medical images.
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