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Official Pytorch implementation for paper LaMoD: Latent Motion Diffusion Model For Myocardial Strain Generation
Open-sourced codes for MiniGPT-4 and MiniGPT-v2 (https://minigpt-4.github.io, https://minigpt-v2.github.io/)
Contrastive unpaired image-to-image translation, faster and lighter training than cyclegan (ECCV 2020, in PyTorch)
This repository uses Cycle GAN for unpaired image to image translation.
为GPT/GLM等LLM大语言模型提供实用化交互接口，特别优化论文阅读/润色/写作体验，模块化设计，支持自定义快捷按钮&函数插件，支持Python和C++等项目剖析&自译解功能，PDF/LaTex论文翻译&总结功能，支持并行问询多种LLM模型，支持chatglm3等本地模型。接入通义千问, deepseekcoder, 讯飞星火, 文心一言, llama2, rwkv, claude2, m…
Class for producing smooth 17 and 18 segment left ventricle plot, recommended by American Heart Association
Class for producing smooth 17 and 18 segment left ventricle plot, recommended by American Heart Association
Automated atrial wall thickness measurement from binary mask images in C++/CUDA
A python program to detect the scar tissues in the Left Ventricle of the human heart and display the same in a Bull's eye view.
An advanced crypto trading bot written in Python
Cryptocurrency quantitative trading system
51bitquant Python数字货币量化交易视频 CCXT框架 爬取交易所数据 比特币量化交易 交易机器人51bitquant tradingbot cryptocurrency quantitative trading btc trading
in this git, i add some new feature to the "https://github.com/laomayi/simDHT"
Framework to enable the inference of a high resolution personalized 4D (3D plus time) surface mesh of the cardiac structures from 2D echocardiography video data.
A deep-learning approach for direct whole-heart mesh reconstruction
医学影像数据集列表 『An Index for Medical Imaging Datasets』
This is the official Pytorch implementation of the paper "Diffusion Models for Implicit Image Segmentation Ensembles".
Virtual Engine for Android(Support 14.0 in business version)
tracking medical datasets, with a focus on medical imaging
[CVPR 2022] Thin-Plate Spline Motion Model for Image Animation.
Combining Faster R-CNN and U-net for efficient medical image segmentation
Robust multimodal image registration via keypoints
[MIDL 2021] Learning Diffeomorphic and Modality-invariant Registration using B-splines