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Nikos Tsiknakis's Projects

nucleisegmentationhedl icon nucleisegmentationhedl

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.

pathology-streaming-pipeline icon pathology-streaming-pipeline

Use streaming to train whole-slides images with single image-level labels, by reducing GPU memory requirements with 99%.

pathology-whole-slide-data icon pathology-whole-slide-data

A package for working with whole-slide data including a fast batch iterator that can be used to train deep learning models.

perinuclearhe icon perinuclearhe

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.

phd-thesis icon phd-thesis

Files to typeset my doctoral thesis (Karolinska Institutet)

spatialscore icon spatialscore

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".

stainlib icon stainlib

Python 3 library for the augmentation & normalization of H&E images

streamingcnn icon streamingcnn

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.

sun-card icon sun-card

Lovelace card for sun component - Home Assistant

tsik.me icon tsik.me

My personal website powered by Jekyll and al-folio.

unet-with-pretrained-encoder icon unet-with-pretrained-encoder

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.

xai icon xai

Papers and code of Explainable AI esp. w.r.t. Image classificiation

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