TransMIL: Transformer based Correlated Multiple Instance Learning for Whole Slide Image Classification

SAM-Med3D: An Efficient General-purpose Promptable Segmentation Model for 3D Volumetric Medical Image

ChatGLM-6B: An Open Bilingual Dialogue Language Model | 开源双语对话语言模型

Implementation of Vision Transformer, a simple way to achieve SOTA in vision classification with only a single transformer encoder, in Pytorch

Grounded SAM: Marrying Grounding DINO with Segment Anything & Stable Diffusion & Recognize Anything - Automatically Detect , Segment and Generate Anything

Unsupervised Learning for Image Registration

AI Toolkit for Healthcare Imaging

Segmentation deep learning ALgorithm based on MONai toolbox: single and multi-label segmentation software developed by QIMP team-Vienna.

医学影像数据集列表 『An Index for Medical Imaging Datasets』

3D ResNets for Action Recognition (CVPR 2018)

Easy-to-use,Modular and Extendible package of deep-learning based CTR models .

scikit-learn: machine learning in Python

Deep Learning for End-to-End Kidney Cancer Diagnosis on Multi-Phase Abdominal Computed Tomography

A Web Player for 360 Video on the Oculus

Image augmentation for machine learning experiments.

Source code for SpatialExtractor-TemporalEncoder-Integration-Classifier (STIC) model

including unet,unet++,attention-unet,r2unet,cenet,segnet ,fcn.

3D-Unet: patched based Pytorch implementation for medical images segmentation

Paper and implementation of UNet-related model.

[MICCAI 2021 Challenge] The official baseline model for MICCAI Challenge: Fast and Low GPU memory Abdominal oRgan sEgmentation (FLARE21)

Show 3D segmentation results with the lightweight myvi.

A pytorch-based deep learning framework for multi-modal 2D/3D medical image segmentation

U-RISC 神经元识别大赛 第一名解决方案

2nd place solution to the Ultra-high Resolution EM Images Segmentation Challenge hosted by BAAI and PKU

Semantic Segmentation Architectures Implemented in PyTorch

The largest collection of PyTorch image encoders / backbones. Including train, eval, inference, export scripts, and pretrained weights -- ResNet, ResNeXT, EfficientNet, NFNet, Vision Transformer (V…

Urbanization detection using computer vision algorithms to reduce the reliance on the data of government surveys, which will speed up the policy making process.

主要存储Datawhale组队学习中“数据挖掘/机器学习”方向的资料。

深度学习入门教程, 优秀文章, Deep Learning Tutorial