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food-segmentation

This is a project that segmented foods in photographs via an active contour algorithm

fpga_based_cnn

FPGA based acceleration of Convolutional Neural Networks. The project is developed by Verilog for Altera DE5 Net platform.

fracsp

frangi3d

Computes vesselness scores for 3-dimensional images.

fu-net

FU-net: Multi-class Image Segmentation using Feedback Weighted U-net

fully-connected-densenets-semantic-segmentation

Fully Connected DenseNet for Image Segmentation (https://arxiv.org/pdf/1611.09326v1.pdf)

fundus-vessel-segmentation-tbme

In this work, we present an extensive description and evaluation of our method for blood vessel segmentation in fundus images based on a discriminatively trained, fully connected conditional random field model. Standard segmentation priors such as a Potts model or total variation usually fail when dealing with thin and elongated structures. We overcome this difficulty by using a conditional random field model with more expressive potentials, taking advantage of recent results enabling inference of fully connected models almost in real-time. Parameters of the method are learned automatically using a structured output support vector machine, a supervised technique widely used for structured prediction in a number of machine learning applications. Our method, trained with state of the art features, is evaluated both quantitatively and qualitatively on four publicly available data sets: DRIVE, STARE, CHASEDB1 and HRF. Additionally, a quantitative comparison with respect to other strategies is included. The experimental results show that this approach outperforms other techniques when evaluated in terms of sensitivity, F1-score, G-mean and Matthews correlation coefficient. Additionally, it was observed that the fully connected model is able to better distinguish the desired structures than the local neighborhood based approach. Results suggest that this method is suitable for the task of segmenting elongated structures, a feature that can be exploited to contribute with other medical and biological applications.

fuzzy-code-cg

remote sensing image classification based on fuzzy method

fuzzy-region-based-active-contour-driven-by-global-and-local-fitting-energy-for-image-segmentation

fvsr-net

Paper"FVSR-Net: An End-to-end Finger Vein Image Scattering Removal Network"

gan_breast_mammography_segmentation

SegNet-cGAN and UNET-cGAN for Breast Mammography Segmentation

gan_fake_finger_vein

GAN을 이용한 fake Fingervein image 생성하기

ganseg

Framework for medical image segmentation using deep neural networks

gastric-cancer-detect

胃癌恶性病变组织检测

gat

Graph Attention Networks (https://arxiv.org/abs/1710.10903)

gbm-robustradiomics

Code accompanying the paper "Radiomics for glioblastoma survival analysis in pre-operative MRI: exploring feature robustness, class boundaries, and machine learning techniques"

gcn

Implementation of Graph Convolutional Networks in TensorFlow

gen-efficientnet-pytorch

Pretrained EfficientNet, MobileNetV3 V2 and V1, MNASNet A1 and B1, FBNet, ChamNet, Single-Path NAS

generalizedmedseg

Recent progress on domain adaption/generalization for medical image segmentation

geodesic_distance

geodesic distance transform of 2d/3d images

gfemr

Gaussian Field Estimator with Manifold Regularization for Retinal Image Registration

glcm_textureverification

globallocalimagecompletion_tf

Globally and Locally Consistent Image Completion

glsepf

Code for a novel active contour model guided by global and local signed energy-based pressure force

gluon-tutorials-zh

通过MXNet/Gluon来动手学习深度学习

gm_challenge

Spinal cord gray matter imaging challenge

gmseg

Spinal cord gray matter segmentation using deep dilated convolutions.

gnnpapers

Must-read papers on graph neural networks (GNN)

gpvesseltracking

Tracking and diameter estimation (segmentation) of retinal vessels using Gaussian process and Radon transform

grabcut-graphcut

Matlab implementation of GrabCut and GraphCut for interactive image segmentation