schmidtchristoph Goto Github PK

100-tiramisu-keras

Keras implementation of The One Hundred Layers Tiramisu: Fully Convolutional DenseNets for Semantic Segmentation

alluvial

Alluvial diagrams

anomalydetection

Anomaly Detection with R

attention-gated-networks

Use of Attention Gates in a Convolutional Neural Network / Medical Image Classification and Segmentation

bibdelete

Delete selected field types in a bibtex file

braingraph

Graph theory analysis of brain MRI data

carvana-challenge

My repository for the Carvana Image Masking Challenge

deep-image-prior

Image restoration with neural networks but without learning.

deeplearning

deepresearch

This repository is the collection of research papers in Deep learning, computer vision and NLP.

directedlouvain

dsb2018_topcoders

DSB2018 [ods.ai] topcoders

fc-densenet

Fully Convolutional DenseNets for semantic segmentation.

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.

horovod

Distributed training framework for TensorFlow, Keras, and PyTorch.

js2graphic

R package for saving JavaScript generated plots as graphics file

keras-cam

Keras implementation of class activation mapping

keras_to_tensorflow

General code to convert a trained keras model into an inference tensorflow model

mask_rcnn

Mask R-CNN for object detection and instance segmentation on Keras and TensorFlow

medical_image_segmentation

Medical image segmentation

models

Models and examples built with TensorFlow

nnunet

plotluck

R tool for automated creation of ggplots. Examines one, two, or three variables and creates, based on their characteristics, a scatter, violin, box, bar, density, hex or spine plot, or a heat map. Also automates handling of observation weights, log-scaling of axes, reordering of factor levels, and overlays of smoothing curves and median lines.

progress

Progress bar in your R terminal

pyinstrument

🚴 Call stack profiler for Python. Shows you why your code is slow!

pytorch-semseg

Semantic Segmentation Architectures Implemented in PyTorch

raw

The missing link between spreadsheets and vector graphics

rbiofabric_original

An R implementation of the BioFabric network visualization tool

rcharts_d3_sankey

rCharts implementation of d3 sankey plugin

reach

R < > Matlab interoperability