剑楠的笔记本
Linux
正在初始化搜索引擎
剑楠的笔记本
Home
Python
Python
Python中的垃圾回收机制
使用gdb调试Python程序
Python中堆的实现和应用
Python使用LUT给图片添加滤镜
常见问题及代码
Python与其他语言进行socket通信
编写更好的Python函数
Python behind the scenes
Python behind the scenes
CPython虚拟机的工作原理
CPython编译器的工作原理
深入CPython源码
Python的字节码如何执行
CPython中的变量如何实现
Python对象系统的工作原理
Paper
Paper
3D Bounding Box Estimation Using Deep Learning and Geometry
3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation
3D \(U2\)-Net：A 3D Universal U-Net for Multi-Domain Medical Image Segmentation.md
A Combined Approach for Accurate and Accelerated Teeth Detection on Cone Beam CT Images
A Lip Sync Expert Is All You Need for Speech to Lip Generation In The Wild
A fully automatic AI system for tooth and alveolar bone segmentation from cone beam CT images
ACPL：Anti-curriculum Pseudo-labelling for Semi-supervised Medical Image Classification
AI enabled Automatic Multimodal Fusion of Cone Beam CT and Intraoral Scans for Intelligent 3D Tooth Bone Reconstruction and Clinical Applications
Align Deep Features for Oriented Object Detection
Aligning Point Cloud Views using Persistent Feature Histograms
An End-to-End Transformer Model for 3D Object Detection
An Image is Worth 16x16 Words：Transformers for Image Recognition at Scale.md
ArcFace：Additive Angular Margin Loss for Deep Face Recognition
Attention Is All You Need
Automated Design of Deep Learning Methods for Biomedical Image Segmentation
Automated head and neck tumor segmentation from 3D PET/CT
Automatic Segmentation of Mandible from Conventional Methods to Deep Learning—A Review
BoostMIS：Boosting Medical Image Semi-supervised Learning with Adaptive Pseudo Labeling and Informative Active Annotation
Boundary loss for highly unbalanced segmentation
Center Sensitive and Boundary Aware Tooth Instance Segmentation and Classification from Cone Beam CT
Deep High-Resolution Representation Learning for Visual Recognition
Deep Hough Voting for 3D Object Detection in Point Clouds
Deep Learning for Medical Image Segmentation：Tricks, Challenges and Future Directions
Deep Learning-Based Automatic Segmentation of Mandible and Maxilla in Multi-Center CT Images
Deep Residual Learning for Image Recognition
Deep learning method for reducing metal artifacts in dental cone-beam CT using supplementary information from intra-oral scan
Densely Connected Convolutional Networks
DiRA：Discriminative, Restorative, and Adversarial Learning for Self-supervised Medical Image Analysis
DirectPose：Direct End-to-End Multi-Person Pose Estimation
End-to-End Object Detection with Transformers
Explicit Box Detection Unifies End-to-End Multi-Person Pose Estimation
Fast Facial Landmark Detection and Applications：A Survey
Fast Point Feature Histograms (FPFH) for 3D registration
Focused Decoding Enables 3D Anatomical Detection by Transformers
Frustum PointNets for 3D Object Detection from RGB-D Data
Fully Convolutional Networks for Semantic Segmentation
Fully automatic integration of dental CBCT images and full-arch intraoral impressions with stitching error correction via individual tooth segmentation and identification
Generalized Decoding for Pixel, Image, and Language
Hierarchical Morphology-Guided Tooth Instance Segmentation from CBCT Images
Highly Accurate Dichotomous Image Segmentation
Image-to-Image Translation with Conditional Adversarial Networks
Integrating Spatial Configuration into Heatmap Regression Based CNNs for Landmark Localization
Joint Training of a Convolutional Network and a Graphical Model for Human Pose Estimation
Learning 3D Representations from 2D Pre-trained Models via Image-to-Point Masked Autoencoders
Mask DINO: Towards A Unified Transformer-based Framework for Object Detection and Segmentation
Mask-Attention-Free Transformer for 3D Instance Segmentation
Masked Autoencoders Are Scalable Vision Learners
Masked Autoencoders As Spatiotemporal Learners
Masked attention Mask Transformer for Universal Image Segmentation
MedSegDiff：Medical Image Segmentation with Diffusion Probabilistic Model
Medical Image Segmentation Using Deep Learning：A Survey
OneFormer3D: One Transformer for Unified Point Cloud Segmentation
OneFormer: One Transformer to Rule Universal Image Segmentation
Optimized U-Net for Brain Tumor Segmentation
Per-Pixel Classification is Not All You Need for Semantic Segmentation
Point-BERT: Pre-training 3D Point Cloud Transformers with Masked Point Modeling
Polarized Self Attention：Towards High quality Pixel wise Regression
Query Refinement Transformer for 3D Instance Segmentation
Real-Time High-Resolution Background Matting
Revisiting nnU-Net for Iterative Pseudo Labeling and Efficient Sliding Window Inference
Rich feature hierarchies for accurate object detection and semantic segmentation
Robust Reconstruction of Indoor Scenes
SUN RGB-D：A RGB-D Scene Understanding Benchmark Suite
Segment Anything
Self-Supervised Pre-Training of Swin Transformers for 3D Medical Image Analysis
Self-Supervised Pretraining of 3D Features on any Point-Cloud
Self-supervised Learning for Pre-Training 3D Point Clouds: A Survey
Superpoint Transformer for 3D Scene Instance Segmentation
Swin Transformer：Hierarchical Vision Transformer using Shifted Windows
Swin-Unet：Unet-like Pure Transformer for Medical Image Segmentation.md
Tooth Instance Segmentation from Cone Beam CT Images through Point based Detection and Gaussian Disentanglement
ToothNet：Automatic Tooth Instance Segmentation and Identification from Cone Beam CT Images
TotalSegmentator：robust segmentation of 104 anatomical structures in CT images.md
TransUNet：Transformers Make Strong Encoders for Medical Image Segmentation
U-Net: Convolutional Networks for Biomedical Image Segmentation
\(U^2\)-Net: Going Deeper with Nested U-Structure for Salient Object Detection
UNETR：Transformers for 3D Medical Image Segmentation.md
UNeXt: MLP-based Rapid Medical Image Segmentation Network
UNet++： A Nested U Net Architecture for Medical Image Segmentation
Very Deep Convolutional Networks for Large-Scale Image Recognition
YOLO9000：Better, Faster, Stronger
YOLOv3：An Incremental Improvement
YOLOv4：Optimal Speed and Accuracy of Object Detection
YOLOv6：A Single-Stage Object Detection Framework for Industrial Applications
You Only Look Once：Unified, Real-Time Object Detection
clDice：A Novel Topology Preserving Loss Function for Tubular Structure Segmentation
nnU-Net: Self-adapting Framework for U-Net-Based Medical Image Segmentation
Linux
Linux
Ubuntu睿频
ATP简介
内网穿透
新建用户
crontab
Ubuntu20.04设置桌面快捷启动图标
Ubuntu20.04挂起/休眠后无法唤醒
frp
Ubuntu离线环境下安装软件
IO多路复用
挂载移动硬盘
常见问题及命令
Linux入侵类问题排查思路
调整Ubuntu交换内存大小
文件解压缩
Ubuntu18 IP配置
Ubuntu18 开机启动
Docker
Docker
搭建ELK
Docker compose
Docker内运行GUI程序
harbor
Docker内无法访问外网
安装docker及英伟达docker
常见问题及命令
ssh连接到docker
Deep Learning
Deep Learning
训练AlphaPose
机器学习评价指标
使用labelme标注人体关键点
损失函数
mlflow
训练MobileNetV2(mmclassification)
英伟达驱动
PyTorch学习笔记
训练SlowFast
Pytorch和MONAI中的transforms
训练YoloV5
训练YoloX
Pycoder's Weekly
Pycoder's Weekly
1-10
11-20
21-30
31-40
41-50
51-60
61-70
71-80
81-90
91-100
101-100
111-120
121-130
131-140
141-150
151-160
161-170
171-180
181-190
191-200
201-210
211-220
221-230
231-240
241-250
251-260
261-270
271-280
281-290
291-300
301-310
311-320
321-330
331-340
341-350
351-360
361-370
371-380
381-390
391-400
401-410
411-420
421-430
431-440
441-450
451-460
461-470
471-480
481-490
491-500
501-510
511-520
521-530
531-540
541-540
551-560
561-570
571-580
581-590
591-600
601-610
611-620
621-630
631-640
641-650
651-660
661-670
LeetCode
LeetCode
1.两数之和
11.盛最多水的容器
15.三数之和
16.最接近的三数之和
18.四数之和
26.删除有序数组中的重复项
27.移除元素
31.下一个排列
33.搜索旋转排序数组
34.在排序数组中查找元素的第一个和最后一个位置
35.搜索插入位置
36.有效的数独
37.解数独
39.组合总和
4.寻找两个正序数组的中位数
Ningx
Ningx
配置
常见问题及命令
Git
Git
Submodule
Supervisor
Medical
Medical
CT图像简介
MONAI
医学影像中的重采样
医学影像文件常见格式
医学影像领域常见名词
方向和体素顺序术语：RAS、LAS、LPI、RPI、XYZ 等等
Other
Other
B样条曲线拟合
Ubuntu下Navicat无限试用
Dvc
Postman请求json中注释
下载论文
卡尔曼滤波
哈希算法
常见三维模型文件介绍
常见问题及命令
机器人路径规划
点云降采样
自适应蒙特卡洛定位
视频中的基础概念
Linux
评论