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12306model

本地识别模型下载

airtoolsii

The AIR Tools II toolbox for MATLAB accompanies the publication "AIR Tools II: algebraic iterative reconstruction methods, improved implementation", Hansen, P. C. & Jørgensen, J. S. Numer Algor (2017).

codes-for-lane-detection

Learning Lightweight Lane Detection CNNs by Self Attention Distillation (ICCV 2019)

coursera-ml-andrewng-notes

吴恩达老师的机器学习课程个人笔记

deep-learning-approach-to-improve-tangential-resolution-in-photoacoustic-tomography

In this project, the tangential resolution in photoacoustic tomography is improved by the deep learning approach.

deep-neural-network-for-bandwidth-enhancement-of-photoacoustic-data

deep-neural-network-photoacoustic

deeplearning-muli-notes

Notes about courses Dive into Deep Learning by Mu Li

dense_unet_keras

Fully Dense UNet implementation in medical image segmentation

dl-approach-to-improve-tangential-resolution-in-photoacoustic-tomography

dl-docker

An all-in-one Docker image for deep learning. Contains all the popular DL frameworks (TensorFlow, Theano, Torch, Caffe, etc.)

drawio-desktop

Official electron build of draw.io

dsp_coursedesign

电子科技大学挑战性课程《信号处理系统》（原信号与系统+数字信号处理）课程课程设计

head-ct-hemorrhage-detection

Machine learning algorithms that detect Brain Hemorrhage in Computed Tomography (CT) imaging

linux-driver-learn

《Linux设备驱动开发详解-基于最新的Linux4.0内核》配套代码

multiyolov5

joint detection and semantic segmentation, based on ultralytics/yolov5,

pa-tomography-xu-wang

Photoacoustic tomography as proposed by Xu and Wang in Python and Rust.

pa_reconstruction

a python program to generate simulating photoacoustic signals, reconstruct it on 2d plane and 3d space

paireconstruction

Photoacoustic Imaging - Image Reconstruction

pam_dataset

The Duke PAM dataset contains OR-PAM images collected at 532 nm, and is managed by Dr. Junie Yao's Photoacoustic Imaging Lab at Duke University.

pam_dl_upsampling

This project contains the continued work to perfect a model for upsampling undersampled Photoacoustic Microscopy (PAM) images.

pasrnet

Photoacoustic super resolution

pat

inverse problem toolbox for hybrid diffusive photoacoutic and optical tomography

pat_codes

patimagereconstruction

Matlab codes for PAT image reconstruction from subsampled data based on a novel regularisation term (Hessian Schatten-norm of the filtered image by Gaussian function), using k-Wave Matlab toolbox, FISTA and ADMM algorithm

photoacoustic-image-reconstruction

Photoacoustic Imaging Reconstruction

python_for_microscopists

https://www.youtube.com/channel/UC34rW-HtPJulxr5wp2Xa04w?sub_confirmation=1

qpatdiff

Quantitative photoacoustic tomography with the diffusion model for light propagation

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.

superiorized-photo-acoustic-non-negative-reconstruction-for-clinical-photoacoustic-imaging

Photoacoustic (PA) imaging can revolutionize medical ultrasound by augmenting it with molecular information. However, clinical translation of PA imaging remains a challenge due to the limited viewing angles and imaging depth. Described here is a new robust algorithm called Superiorized Photo-Acoustic Non-NEgative Reconstruction (SPANNER), designed to reconstruct PA images in real-time and to address these limitations. The method utilizes precise forward modeling of the PA propagation and reception of signals while accounting for the effects of acoustic absorption, element size, shape, and sensitivity, as well as the transducer's impulse response and directivity pattern. A fast superiorized conjugate gradient algorithm is used for inversion. SPANNER is compared to three reconstruction algorithms: delay-and-sum (DAS), universal back-projection (UBP), and model-based reconstruction (MBR). All four algorithms are applied to both simulations and experimental data acquired from tissue-mimicking phantoms, ex vivo tissue samples, and in vivo imaging of the prostates in patients. Simulations and phantom experiments highlight the ability of SPANNER to improve contrast to background ratio by up to 20 dB compared to all other algorithms, as well as a 3-fold increase in axial resolution compared to DAS and UBP. Applying SPANNER on contrast-enhanced PA images acquired from prostate cancer patients yielded a statistically significant difference before and after contrast agent administration, while the other three image reconstruction methods did not, thus highlighting SPANNER's performance in differentiating intrinsic from extrinsic PA signals and its ability to quantify PA signals from the contrast agent more accurately.