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Name: Nikos Tsiknakis
Type: User
Company: Karolinska Institutet
Bio: PhD student on computational pathology at Oncology-Pathology, Karolinska Institutet.
Twitter: NTsiknakis
Location: Stockholm, Sweden
Blog: tsik.me
Name: Nikos Tsiknakis
Type: User
Company: Karolinska Institutet
Bio: PhD student on computational pathology at Oncology-Pathology, Karolinska Institutet.
Twitter: NTsiknakis
Location: Stockholm, Sweden
Blog: tsik.me
Nuclei segmentation performed on the digitized H&E-stained images of whole slide images (WSI). It is a deep learning model (DL) based on a GAN architecture.
H&E tailored Randaugment: automatic data augmentation policy selection for H&E-stained histopathology.
Pieces of code likely to be useful as part of larger projects
Use streaming to train whole-slides images with single image-level labels, by reducing GPU memory requirements with 99%.
A package for working with whole-slide data including a fast batch iterator that can be used to train deep learning models.
Set of features extracted from Whole Slide images (WSI) patches, related to the perimetral tissue region surrounding individual tumor cells, as well as the cell morphometrics.
Files to typeset my doctoral thesis (Karolinska Institutet)
An implementation of Unet for pytorch designed for digital pathology segmentation
This is a template project for QuPath development on IntelliJ
R script for calculating the SpatialScore as described in our manuscript: "Immune cell topography predicts response to PD-1 blockade in cutaneous T cell lymphoma".
Python 3 library for the augmentation & normalization of H&E images
To train deep convolutional neural networks, the input data and the activations need to be kept in memory. Given the limited memory available in current GPUs, this limits the maximum dimensions of the input data. Here we demonstrate a method to train convolutional neural networks while holding only parts of the image in memory.
Lovelace card for sun component - Home Assistant
My personal website powered by Jekyll and al-folio.
Tumor segmentation on H&E-stained images based on UNET
The repository contains the Jupyter Notebook that perform semantic segmentation using the famous U-Net. The encoder of the U-Net is replaced with the pretrained encoder.
Papers and code of Explainable AI esp. w.r.t. Image classificiation
Stop relying on GUI; CLI **ROCKS**
A declarative, efficient, and flexible JavaScript library for building user interfaces.
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
An Open Source Machine Learning Framework for Everyone
The Web framework for perfectionists with deadlines.
A PHP framework for web artisans
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
Some thing interesting about web. New door for the world.
A server is a program made to process requests and deliver data to clients.
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
Some thing interesting about visualization, use data art
Some thing interesting about game, make everyone happy.
We are working to build community through open source technology. NB: members must have two-factor auth.
Open source projects and samples from Microsoft.
Google ❤️ Open Source for everyone.
Alibaba Open Source for everyone
Data-Driven Documents codes.
China tencent open source team.