mjbeyeler

mjbeyeler's Stars

• torvalds/linux

  Linux kernel source tree

  Language:C183k7.8k054.1k

• microsoft/WSL

  Issues found on WSL

  Language:PowerShell17.5k56910.9k822

• signalapp/Signal-Desktop

  A private messenger for Windows, macOS, and Linux.

  Language:TypeScript14.7k4275.9k2.7k

• facebookresearch/mae

  PyTorch implementation of MAE https//arxiv.org/abs/2111.06377

  Language:Python7.3k571921.2k

• cxli233/FriendsDontLetFriends

  Friends don't let friends make certain types of data visualization - What are they and why are they bad.

  Language:R6.4k1147248

• bioconda/bioconda-recipes

  Conda recipes for the bioconda channel.

  Language:Shell1.6k971.9k3.3k

• vcftools/vcftools

  A set of tools written in Perl and C++ for working with VCF files, such as those generated by the 1000 Genomes Project.

  Language:C++50432191148

• rmaphoh/RETFound_MAE

  RETFound - A foundation model for retinal image

  Language:Jupyter Notebook363153080

• EFS-OpenSource/calibration-framework

  The net:cal calibration framework is a Python 3 library for measuring and mitigating miscalibration of uncertainty estimates, e.g., by a neural network.

  Language:Python34775042

• thaider/Tweeki

  MediaWiki skin based on Twitter's Bootstrap

  Language:PHP1281424231

• seva100/optic-nerve-cnn

  Code repository for a paper "Optic Disc and Cup Segmentation Methods for Glaucoma Detection with Modification of U-Net Convolutional Neural Network"

  Language:Jupyter Notebook120111658

• stekhoven/missForest

  missForest is a nonparametric, mixed-type imputation method for basically any type of data for the statistical software R.

  Language:R9173324

• BergmannLab/MONET

  MONET : MOdularising NEtwork Toolbox - https://doi.org/10.1093/bioinformatics/btaa236

  Language:GAP4264415

• AI4SCR/ATHENA

  Language:Jupyter Notebook31246

• mwagner9/BEGPUThinning

  Language:Cuda2810

• kritiyer/AngioNet

  Semantic segmentation network for X-ray angiography images, based on Deeplabv3+

  Language:Python26328

• Mohitasudani/Segthor19-using-ResU-net

  The recent advances in the field of computer vision has led to the wide use of Convolutional Neural Networks (CNNs) in organ segmentation of computed tomography (CT) images. Image guided radiation therapy requires the accurate segmentation of organs at risk (OARs). In this paper, we propose a 2D U-Net network to automatically segment thoracic organs at risk in computed tomography (CT) images. The architecture consists of a down sampling path to capture features and a symmetric up sampling path to obtain precise localization. SegTHOR19 is a competition timed to the conference IEEE ISBI 2019 that addresses the problem of organs at risk segmentation in Computed Tomography (CT) images. In the SegTHOR19 challenge, 40 CT scans with 4 thoracic organs (i.e., esophagus, heart, trachea and aorta) were used for training [1]. We experimented with both 2D U-net and 2D U-Net with Resnet18 architecture to train the networks. Our best results were obtained by using 2D Convolutional U-Net with ResNet18.

  Language:Jupyter Notebook18209

• BergmannLab/PascalX

  Language:Python174105

• roman-tremmel/ggfastman

  Fast manhattenplots using ggplot2

  Language:R9221

• sib-swiss/advanced-statistics

  Webpage for SIB's advanced statistics course

  7502

• Taiwan-CVAI/HeaortaNet

  HeaortaNet is a pre-trained model for 3D segmentation of heart and aorta from non-contrast chest CT.

  Language:Python7100

• BergmannLab/metabomodules-docker

  Language:MATLAB23110