qianjinfighter

qianjinfighter

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LITS2017_UNet2d

A demo to learn to use UNet-2D to solve the liver segmentation task.

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MedicalSeg

3D Medical Image Segmentation Models,集成各种医学图像分割模型的小框架,主要是3D,持续更新...

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SwinMR

This is the official implementation of our proposed SwinMR

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Transformer-in-Computer-Vision

A paper list of some recent Transformer-based CV works.

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3D-CNN-Segmentation

Applying 3D CNN for bio-medical images segmentation with 3D-Unet, Residual 3D-Unet and Recurrent Residual 3D-Unet (R2U3D) implemented in PyTorch.

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A4SM

Archive for Self-supervised learning in Medical images (A4SM).

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AniFormer

Source code of our BMVC 2021 paper: AniFormer: Data-driven 3D Animation with Transformer

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Attention3d-codebase

Implementation of 3D attention mechanisms based on https://github.com/LeftAttention/Attention-Codebase. Thanks to LeftAttetnion for sharing the implementation of 2D attention mechanisms.

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Awesome_Transformer_for_medical_image_analysis

Awesome_Transformer_for_medical_image_analysis

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boundary-loss

Official code for "Boundary loss for highly unbalanced segmentation", runner-up for best paper award at MIDL 2019. Extended version in MedIA, volume 67, January 2021.

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compound-loss-pytorch

Compound loss for PyTorch

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CoTr

[MICCAI2021] CoTr: Efficiently Bridging CNN and Transformer for 3D Medical Image Segmentation

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DAT

Repository of Vision Transformer with Deformable Attention (CVPR2022)

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LeetCode-Go

✅ Solutions to LeetCode by Go, 100% test coverage, runtime beats 100% / LeetCode 题解

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leetcode-master

《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!🚀

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MIScnn

A framework for Medical Image Segmentation with Convolutional Neural Networks and Deep Learning

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pacgo

A Pac Man clone written in Go (with emojis!)

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SCUNet

Practical Blind Denoising via Swin-Conv-UNet and Data Synthesis

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SSL4MIS

Semi Supervised Learning for Medical Image Segmentation, a collection of literature reviews and code implementations.

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starcharts

Plot your repository stars over time.

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