This workflow represents the Default ML Pipeline for AutoML feature from MLme. Machine Learning Made Easy (MLme) is a novel tool that simplifies machine learning (ML) for researchers. By integrating four essential functionalities, namely data exploration, AutoML, CustomML, and visualization, MLme fulfills the diverse requirements of researchers while eliminating the need for extensive coding efforts. MLme serves as a valuable resource that empowers researchers of all technical levels to leverage ...

IDR is based on OMERO and thus all what we show in this notebook can be easily adjusted for use against another OMERO server, e.g. your institutional OMERO server instance.

The main objective of this notebook is to demonstrate how public resources such as the IDR can be used to train your neural network or validate software tools.

The authors of the PLOS Biology paper, "Nessys: A new set of tools for the automated detection of nuclei within intact tissues and dense 3D cultures" published in August ...

Learning objectives

• Read data to analyse from an object store.
• Analyse data in parallel using Dask.
• Show how to use public resources to train neural network.
• Load labels associated to the original data
• Compare results with ground truth.

The authors of the PLOS Biology paper, "Nessys: A new set of tools for the automated detection of nuclei within intact tissues and dense 3D cultures" published in August 2019: https://doi.org/10.1371/journal.pbio.3000388, considered several image ...

The image is referenced in the paper "NesSys: a novel method for accurate nuclear segmentation in 3D" published August 2019 in PLOS Biology: https://doi.org/10.1371/journal.pbio.3000388 and can be viewed online in the Image Data Resource.

This original image was converted into the Zarr format. The analysis results produced by the authors of the paper were converted into labels and linked to the Zarr file which was placed into a public ...

ABR_Threshold_Detection

What is this?

This code can be used to automatically determine hearing thresholds from ABR hearing curves.

One of the following methods can be used for this purpose:

• neural network (NN) training,
• calibration of a self-supervised sound level regression (SLR) method

on given data sets with manually determined hearing thresholds.

Installation:

Run inside the src directory:

Installation as python package

pip install -e ./src (Installation as python
...