u-net-test's starred repositories
Universal-Deep-Beamformer-for-Robust-Ultrasound-Imaging
Computer code and dataset for "Universal Deep Beamformer for Robust Ultrasound Imaging"
Ultrasound_Elastography
It is the code from the Hassan Rivaz's paper .
FloWave.US
Matlab Program for Automated Ultrasound Blood Flow Analysis
Plane_Wave_Ultrasound_Stolt_F-K_Migration.github.io
MATLAB, Python, and CUDA Implementations of Plane Wave Ultrasound Imaging with Stolt's f-k Migration (Original MATLAB code and plane wave data came from here: http://www.biomecardio.com/pageshtm/tools/toolsen.htm) [Reference: Garcia D et al. Stolt's f-k migration for plane wave ultrasound imaging. IEEE UFFC, 2013;60:1853-1867.]
us-beamform-linarray
Ultrasound beamforming using a linear array in Python and Rust.
ultrasoundsim
Experimenting with Ultrasound simulation software packages
CardiacUltrasoundPhaseEstimation
This repository contains an image-based instantaneous phase estimation method for gating and temporal super-resolution of cardiac ultrasound
Semantic-Segmentation_Multiple-Class
Classify multiple objects pixel by pixel with semantic segmantation technique (Trained with Cityscape-Dataset)
conv_arithmetic
A technical report on convolution arithmetic in the context of deep learning
part-based-RCNN
Codes and pretrained model for ECCV 14 paper 'Part based RCNNs for fine-grained category detection'
sliding_window
Python package to run sliding window on numpy array
Nerve-Segmentation
Image recognition for nerves from ultrasound images using a sliding window CNN
vehicle_detection_hog_svm
Vehicle detection using HOG + SVM and sliding windows
Object-detection-with-deep-learning-and-sliding-window
Introduces an approach for object detection in an image with sliding window. The repository contains three files, make_data.py reads the image in gray scale and converts the image into a numpy array. The labels are also appended based on the file name. In this case, if the file name starts with "trn", then 1 is appended else 0. Finally, all the images and labels are saved into .npy file. The test-model-1.py file loads the images and converts the labels into two categories as we are doing binary classification of images. The model is built using keras with theano as backend. In this case, the best training accuracy was 80% since the data was just 500 images and the testing accuracy was 67%
Keras-ResNeXt
Implementation of ResNeXt models from the paper Aggregated Residual Transformations for Deep Neural Networks in Keras 2.0+.
keras-resnet-segmentation
Semantic segmentation implemented with Keras based on ResNet
keras-adversarial
Keras Generative Adversarial Networks
segmentation_keras
DilatedNet in Keras for image segmentation
lung-segmentation-2d
Lung fields segmentation on CXR images using convolutional neural networks.