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This is a repository containing code to Paper "Optimized High Resolution 3D Dense-U-Net Network for Brain and Spine Segmentation" published at MDPI Applied sciences journal - https://www.mdpi.com/2076-3417/9/3/404 .
3D Medical Image Segmentation Pytorch
3D ResNets for Action Recognition (CVPR 2018)
MRI image of rectal cancer segmentation using3D U-net baseline
3D visualizer for medical image segmentation.
3DUNet implemented with pytorch
Use of Attention Gates in a Convolutional Neural Network / Medical Image Classification and Segmentation
This repo is the source code for [BiTr-Unet: a CNN-Transformer Combined Network for MRI Brain Tumor Segmentation].
Predicting age regression from brain MRI with 3D ResUnets and ResNext implemented on PyTorch
深度学习500问,以问答形式对常用的概率知识、线性代数、机器学习、深度学习、计算机视觉等热点问题进行阐述,以帮助自己及有需要的读者。 全书分为18个章节,50余万字。由于水平有限,书中不妥之处恳请广大读者批评指正。 未完待续............ 如有意合作,联系[email protected] 版权所有,违权必究 Tan 2018.06
Official Pytorch Code of KiU-Net for Image/3D Segmentation - MICCAI 2020 (Oral), IEEE TMI
The Medical Segmentation Toolkit is a light-weight segmentation engine which contains training and inference framework in dealing with 3D medical images segmentation.
OpenMMLab Semantic Segmentation Toolbox and Benchmark.
https://www.youtube.com/channel/UC34rW-HtPJulxr5wp2Xa04w?sub_confirmation=1
3D medical image semantic segmentation with Pytorch.
3D U-Net model for volumetric semantic segmentation written in pytorch
[MICCAI2021] CoTr: Efficiently Bridging CNN and Transformer for 3D Medical Image Segmentation
An elegant \LaTeX\ résumé template. 大陆镜像 https://gods.coding.net/p/resume/git
Segmentation models with pretrained backbones. PyTorch.
Semi Supervised Learning for Medical Image Segmentation, a collection of literature reviews and code implementations.
第七届“泰迪杯”数据挖掘挑战赛 B题:直肠癌淋巴结转移的智能诊断 初学小白的解决方案
This repo provides the official code for : 1) TransBTS: Multimodal Brain Tumor Segmentation Using Transformer (https://arxiv.org/abs/2103.04430) , accepted by MICCAI2021. 2) TransBTSV2: Towards Better and More Efficient Volumetric Segmentation of Medical Images(https://arxiv.org/abs/2201.12785).
In recent years, the mortality rate of patients with rectal cancer is increasing. CT rectal examination is of great value in the survival rate of patients with early rectal cancer. Based on this, the project firstly used the CT image of rectal cancer and its corresponding mask image as the research object, and proposed a rectal tumor segmentation model combining U-Net and DenseNet. This model makes full use of the ability of neural network feature learning to effectively extract the rectum. Tumor characteristics information, accurate segmentation of the rectal tumor area, the experimental accuracy reached more than 90%. Furthermore, a rectal tumor classification model based on 3D convolutional neural network is proposed. This model uses the lymph nodes of the rectal tumor region to transfer to the research object, constructs a three-dimensional tensor of the tumor region information, and obtains the classification result of the rectal tumor. The classification accuracy is achieved. 65%.
UNETR++: Delving into Efficient and Accurate 3D Medical Image Segmentation
A declarative, efficient, and flexible JavaScript library for building user interfaces.
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
An Open Source Machine Learning Framework for Everyone
The Web framework for perfectionists with deadlines.
A PHP framework for web artisans
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
Some thing interesting about web. New door for the world.
A server is a program made to process requests and deliver data to clients.
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
Some thing interesting about visualization, use data art
Some thing interesting about game, make everyone happy.
We are working to build community through open source technology. NB: members must have two-factor auth.
Open source projects and samples from Microsoft.
Google ❤️ Open Source for everyone.
Alibaba Open Source for everyone
Data-Driven Documents codes.
China tencent open source team.