👍 👍 👍 高度仿制大麦网售票系统，使用 SpringCloudAlibaba、Kafka、Redis、Sentinel、ElasticSearch、ShardingSphere 等架构，实现 从抢票到生成订单完成支付 的整个流程，并包含各种高并发难题的实际落地解决方案。是面试、就业、提高技术的不二选择！

📃 开箱即用的 Obsidian / Typora 简历

A custom RPC framework implemented by Netty+Kyro+Zookeeper.（一款基于 Netty+Kyro+Zookeeper 实现的自定义 RPC 框架-附详细实现过程和相关教程。）

一个简单的RPC框架的实现

公务员从入门到上岸，最佳程序员公考实践教程

Medical Image Segmentation with Diffusion Model

Implementation of MedSegDiff in Pytorch - SOTA medical segmentation using DDPM and filtering of features in fourier space

Segment Anything in Medical Images

M3D: Advancing 3D Medical Image Analysis with Multi-Modal Large Language Models

A cloud-native Go microservices framework with cli tool for productivity.

《代码随想录》LeetCode 刷题攻略：200道经典题目刷题顺序，共60w字的详细图解，视频难点剖析，50余张思维导图，支持C++，Java，Python，Go，JavaScript等多语言版本，从此算法学习不再迷茫！🔥🔥 来看看，你会发现相见恨晚！🚀

Uber Go 语言编码规范中文版. The Uber Go Style Guide .

[ICLR 2024] Supervised Pre-Trained 3D Models for Medical Image Analysis (9,262 CT volumes + 25 annotated classes)

🔥🔥「InterviewGuide」是阿秀从校园->职场多年计算机自学过程的记录以及学弟学妹们计算机校招&秋招经验总结文章的汇总，包括但不限于C/C++ 、Golang、JavaScript、Vue、操作系统、数据结构、计算机网络、MySQL、Redis等学习总结，坚持学习，持续成长！

🤗 Diffusers: State-of-the-art diffusion models for image and audio generation in PyTorch and FLAX.

Use PEFT or Full-parameter to finetune 300+ LLMs or 80+ MLLMs. (Qwen2, GLM4v, Internlm2.5, Yi, Llama3.1, Llava-Video, Internvl2, MiniCPM-V-2.6, Deepseek, Baichuan2, Gemma2, Phi3-Vision, ...)

BBDM: Image-to-image Translation with Brownian Bridge Diffusion Models

TarGAN - Official PyTorch Implementation (MICCAI 2021)

Semantic Image Synthesis with SPADE

Adaptive Latent Diffusion Model for 3D Medical Image

PyTorch Implementation of the ICCV 2023 paper: Generalized Differentiable RANSAC ($\nabla$-RANSAC).

Generalizing Spatial Transformers to Projective Geometry with Applications to 2D/3D Registration

Open-source 2D/3D registration datasets and dataloaders for DiffDRR

[CVPR 2024] Intraoperative 2D/3D registration via differentiable X-ray rendering

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

Common 2D and 3D image registration methods such as rigid, affine, and flow field for PyTorch.

Affine medical image registration using deep learning

[MICCAI2023] NICE-Trans: Non-iterative Coarse-to-fine Transformer Networks for Joint Affine and Deformable Image Registration