Christoph Schmidt's repositories
js2graphic
R package for saving JavaScript generated plots as graphics file
100-tiramisu-keras
Keras implementation of The One Hundred Layers Tiramisu: Fully Convolutional DenseNets for Semantic Segmentation
AnomalyDetection
Anomaly Detection with R
Attention-Gated-Networks
Use of Attention Gates in a Convolutional Neural Network / Medical Image Classification and Segmentation
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.
DeepResearch
This repository is the collection of research papers in Deep learning, computer vision and NLP.
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.
keras_to_tensorflow
General code to convert a trained keras model into an inference tensorflow model
medical_image_segmentation
Medical image segmentation
pyinstrument
🚴 Call stack profiler for Python. Shows you why your code is slow!
pytorch-semseg
Semantic Segmentation Architectures Implemented in PyTorch
retina-unet
Retina blood vessel segmentation with a convolutional neural network
Retina-VesselNet
A DenseBlock-Unet for Retinal Blood Vessel Segmentation
TernausNet
UNet model with VGG11 encoder pre-trained on Kaggle Carvana dataset
TernausNetV2
TernausNetV2: Fully Convolutional Network for Instance Segmentation