NirvanaChan

NirvanaChan's Stars

• facebookresearch/segment-anything

  The repository provides code for running inference with the SegmentAnything Model (SAM), links for downloading the trained model checkpoints, and example notebooks that show how to use the model.

  Language:Jupyter Notebook48.3k3146795.7k

• rasbt/LLMs-from-scratch

  Implement a ChatGPT-like LLM in PyTorch from scratch, step by step

  Language:Jupyter Notebook36.4k3941094.6k

• zbezj/HEU_KMS_Activator

  30.9k3941573.2k

• MIC-DKFZ/nnUNet

  Language:Python6.1k762.2k1.8k

• bowang-lab/MedSAM

  Segment Anything in Medical Images

  Language:Jupyter Notebook3.1k24300429

• Tencent/MedicalNet

  Many studies have shown that the performance on deep learning is significantly affected by volume of training data. The MedicalNet project provides a series of 3D-ResNet pre-trained models and relative code.

  Language:Python2k6383418

• tannerhelland/PhotoDemon

  A free portable photo editor focused on pro-grade features, high performance, and maximum usability.

  Language:Visual Basic 6.01.5k55436207

• SuperMedIntel/Medical-SAM-Adapter

  Adapting Segment Anything Model for Medical Image Segmentation

  Language:Python1k1010192

• rasbt/LLM-workshop-2024

  A 4-hour coding workshop to understand how LLMs are implemented and used

  Language:Jupyter Notebook803393234

• MrGiovanni/SyntheticTumors

  [CVPR 2023] Label-Free Liver Tumor Segmentation

  Language:Python33581526

• QiTianyu-0403/OpenCVImage

  📸 A project covering all basic operations of OpenCV based on Qt

  Language:C++2964399

• linhandev/medSeg

  Medical Image Segmentation Toolkit based on PaddlePaddle - 基于paddle的医学影像分割框架

  Language:Python1224725

• Luffy03/Large-Scale-Medical

  [CVPR 2024 Extension] 160K volumes (42M slices) datasets, new segmentation datasets, 31M-1.2B pre-trained models, various pre-training recipes, 50+ downstream tasks implementation

  Language:Python1155277

• mdozmorov/TCGAsurvival

  Scripts to analyze TCGA data

  Language:R115101742

• yulinghan/ImageQualityEnhancement

  Language:C++701015

• junqiangchen/VNet

  Prostate MR Image Segmentation 2012

  Language:Python596427

• FT-ZHOU-ZZZ/CMTA

  Here is the official implementation of the paper "Cross-Modal Translation and Alignment for Survival Analysis"

  Language:Python53167

• BCV-Uniandes/UltraGAN

  Ultrasound Enhancement through Adversarial Generation - Workshop on Simulation and Synthesis in Medical Imaging - SASHIMI 2020

  Language:Python48448

• ischintsan/cuda_by_example

  GPU高性能编程CUDA实战随书代码

  Language:C31105

• ShenQM/Image-Processing

  Language:MATLAB15219

• mlf-core/liver-ct-segmentation

  Segmentation of liver computed tomography images with a UNET.

  Language:Python14104

• sonientaegi/matlab

  Language:Matlab12306

• raycaohmu/E2EFP-MIL

  Language:Python9111

• Monologuethl/ultrasound_image

  超声图像处理

  Language:Python5202

• yazanmaalla/Edge-preserving-filtering

  Edge preserving filtering for ultrasound images: Comparison between: Isotropic Gaussian filter, Bilateral filter, Anisotropic diffusion filter.

  Language:Python5100

• ziqinLin/USenhance2023

  Language:Python5113

• johnnydfci/DL-parenchymal

  Deep Learning-selected Liver Parenchyma for Categorizing Hepatic Steatosis on Multinational Chest CT

  Language:Python4211

• Mohamad-hourani/Joint-deconvolution-of-fundamental-and-harmonic-ultrasound-images

  In this code, method of joint deconvolution of fundamental and harmonic ultrasound images based on the ADMM framework. The method is applied on simulation images (non blind and blind approach) and real data (blind deconvolution). For more details please see (M. Hourani, A. Basarab, D. Kouamé and J. -Y. Tourneret, "Ultrasound Image Deconvolution Using Fundamental and Harmonic Images," in IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, vol. 68, no. 4, pp. 993-1006, April 2021, doi: 10.1109/TUFFC.2020.3028166.)

  Language:MATLAB2104

• YingnanMa/Edge-Guided-Denoising-CNN-For-Ultrasound-Images

  Accepted by ICPR 2020

  211

• kvarun95/cshg

  Compressive Second Harmonic Generation Imaging

  Language:HTML10