Wang Kun (DLWK)

DLWK

Geek Repo

Company:CQU

Location:Chongqing

Home Page:ISSE AI Lab & CSAI

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Wang Kun's repositories

EANet

[PR2022] The Code of “EANet: Iterative Edge Attention Network for Medical Image Segmentation”

CTF-Net

[ISBI'20] The Code of “CTF-Net: Retinal vessel segmentation via Deep coarse-to-fine supervision network”

CGRNet

[BSPC22]The Code of “CGRNet: Contour-Guided Graph Reasoning Network for Ambiguous Biomedical Image Segmentation”

GSAL

[PR2023] The Code of "GSAL: Geometric Structure Adversarial Learning for Robust Medical Image Segmentation"

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Context-aware-segmentation

The repository contains various gan based architectures for segmentation

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MGSA-Net

[BSPC21] The Code of “MGSANet: Multi-Granularity Scale-Aware Networks for Hard Pixels Segmentation of Pulmonary Nodules”

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Multi-Scale-Attention

Code for our paper "Multi-scale Guided Attention for Medical Image Segmentation"

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Awesome-Text-to-Image

A Survey on Text-to-Image Generation/Synthesis.

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axial-deeplab

This is a PyTorch re-implementation of Axial-DeepLab (ECCV 2020 Spotlight)

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CV_ToolBox

data preprocess for cv, including data aug (general and cutout) for object detection, KITTI, COCO data to VOC

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dlwpt-code

Code for the book Deep Learning with PyTorch by Eli Stevens and Luca Antiga.

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GAML

GAML: Geometry-Aware Mutual Learning for Polyp Segmentation

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Learning-Notes

Personal records, notes and resources on Machine Learning.

learning_research

本人博士期间的科研经验

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MB-DCNN

[TMI2020]A Mutual Bootstrapping Model for Automated Skin Lesion Segmentation and Classification.

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NoduleNet

[MICCAI' 19] NoduleNet: Decoupled False Positive Reduction for Pulmonary Nodule Detection and Segmentation

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SSL4MIS

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

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awesome-chatgpt-prompts-zh

ChatGPT 中文调教指南。怎么让它听你的话。

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MSNet

(MICCAI 2021) Automatic Polyp Segmentation via Multi-scale Subtraction Network

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pytorch_geometric_temporal

PyTorch Geometric Temporal: Spatiotemporal Signal Processing with Neural Machine Learning Models (CIKM 2021)

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