Artificial intelligence (44)

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Artificial intelligence (44)#

AI ML DL in Bioimage Analysis - Webinar#

Yannick KREMPP

Published 2024-11-14

Licensed UNKNOWN

A review of the tools, methods and concepts useful for biologists and life scientists as well as bioimage analysts.

Tags: Artificial Intelligence, Bioimage Analysis

Content type: Video, Slides, Webinar

https://www.youtube.com/watch?v=TJXNMIWtdac


AI4Life teams up with Galaxy Training Network (GTN) to enhance training resources#

Caterina Fuster-Barceló

Licensed UNKNOWN

Tags: Artificial Intelligence, Workflow Engine, Bioimage Analysis

Content type: Documentation

https://ai4life.eurobioimaging.eu/ai4life-teams-up-with-galaxy-training-network-gtn-to-enhance-training-resources/


Artificial Intelligence for Digital Pathology#

Jakob Nikolas Kather, Faisal Mahmood, Florian Jug

Published 2024-11-08

Licensed UNKNOWN

How can artificial intelligence be used for digital pathology?

Tags: Artificial Intelligence

Content type: Video

https://www.youtube.com/watch?v=Om9tl4Dh2yw


BIDS-lecture-2024#

Robert Haase

Licensed CC-BY-4.0

Training resources for Students at Uni Leipzig who want to dive into bio-image data science with Python. The material developed here between April and July 2024.

Tags: Bioimage Analysis, Artificial Intelligence, Python

Content type: Github Repository

ScaDS/BIDS-lecture-2024


Bio-image Analysis Code Generation using bia-bob#

Robert Haase

Published 2024-10-09

Licensed CC-BY-4.0

In this presentation I introduce bia-bob, an AI-based code generator that integrates into Jupyter Lab and allows for easy generation of Bio-Image Analysis Python code. It highlights how to get started with using large language models and prompt engineering to get high-quality bio-image analysis code.

Tags: Artificial Intelligence, Bioimage Analysis

https://zenodo.org/records/13908108

https://doi.org/10.5281/zenodo.13908108


Bio-image Analysis with the Help of Large Language Models#

Robert Haase

Published 2024-03-13

Licensed CC-BY-4.0

Large Language Models (LLMs) change the way how we use computers. This also has impact on the bio-image analysis community. We can generate code that analyzes biomedical image data if we ask the right prompts. This talk outlines introduces basic principles, explains prompt engineering and how to apply it to bio-image analysis. We also compare how different LLM vendors perform on code generation tasks and which challenges are ahead for the bio-image analysis community.

Tags: Artificial Intelligence, Python

Content type: Slides

https://zenodo.org/records/10815329

https://doi.org/10.5281/zenodo.10815329


Bio-image Data Science#

Robert Haase

Licensed CC-BY-4.0

This repository contains training resources for Students at Uni Leipzig who want to dive into bio-image data science with Python.

Tags: Research Data Management, Artificial Intelligence, Bioimage Analysis, Python

Content type: Notebook

ScaDS/BIDS-lecture-2024


Bio-image Data Science Lectures @ Uni Leipzig / ScaDS.AI#

Robert Haase

Licensed CC-BY-4.0

These are the PPTx training resources for Students at Uni Leipzig who want to dive into bio-image data science with Python. The material developed here between April and July 2024.

Tags: Bioimage Analysis, Artificial Intelligence, Python

Content type: Slides

https://zenodo.org/records/12623730


BioEngine#

Jeremy Metz, Beatriz Serrano-Solano, Wei Ouyang

Licensed UNKNOWN

BioEngine is a cloud infrastructure to run BioImage model zoo based workflows in the cloud.

Tags: Artificial Intelligence, Workflow Engine

Content type: Publication

https://ai4life.eurobioimaging.eu/announcing-bioengine/


BioEngine Documentation#

Wei Ouyang, Nanguage, Jeremy Metz, Craig Russell

Licensed MIT

BioEngine, a Python package designed for flexible deployment and execution of bioimage analysis models and workflows using AI, accessible via HTTP API and RPC.

Tags: Workflow Engine, Artificial Intelligence, Python

Content type: Documentation

https://bioimage-io.github.io/bioengine/#/



Bioimage Model Zoo#

Licensed UNKNOWN

Tags: Bioimage Analysis, Artificial Intelligence

Content type: Collection

https://bioimage.io/


Building a Bioimage Analysis Workflow using Deep Learning#

Estibaliz Gómez-de-Mariscal

Licensed UNKNOWN

Tags: Artificial Intelligence, Bioimage Analysis

Content type: Slides

esgomezm/NEUBIAS_chapter_DL_2020


CARE/Stardist tutorials for EMBO Practical Course — Computational optical biology 2022#

Martin Weigert

Licensed UNKNOWN

Tags: Python, Artificial Intelligence, Bioimage Analysis

Content type: Notebook

maweigert/embo_2022


CSBDeep and StarDist @ I2K 2020#

Martin Weigert, Uwe Schmidt

Licensed UNKNOWN

Tags: Python, Artificial Intelligence, Bioimage Analysis

Content type: Notebook

maweigert/stardist-i2k


Collection of teaching material for deep learning for (biomedical) image analysis#

Constantin Pape

Licensed MIT

Tags: Artificial Intelligence, Bioimage Analysis

constantinpape/dl-teaching-resources


Course on Deep Learning for imaging using PyTorch#

Guillaume Witz

Licensed UNKNOWN

Tags: Python, Bioimage Analysis, Artificial Intelligence

Content type: Notebook

guiwitz/DLImaging


Creating a Research Data Management Plan using chatGPT#

Robert Haase

Published 2023-11-06

Licensed CC-BY-4.0

In this blog post the author demonstrates how chatGPT can be used to combine a fictive project description with a DMP specification to produce a project-specific DMP.

Tags: Research Data Management, Artificial Intelligence

Content type: Blog Post

https://focalplane.biologists.com/2023/11/06/creating-a-research-data-management-plan-using-chatgpt/


DEEP NAPARI : Napari as a tool for deep learning project management#

Herearii Metuarea, David Rousseau, Pejman Rasti, Valentin Gilet

Licensed UNKNOWN

Tags: Artificial Intelligence, Bioimage Analysis

Content type: Notebook

hereariim/DEEP-NAPARI


DL@MBL 2021 Exercises#

Jan Funke, Constantin Pape, Morgan Schwartz, Xiaoyan

Licensed UNKNOWN

Tags: Artificial Intelligence, Bioimage Analysis

Content type: Slides, Notebook

JLrumberger/DL-MBL-2021


Deep Learning Based Segmentation For Biologists#

Licensed AGPL-3.0

Tags: Python, R, Artificial Intelligence

Content type: Notebook

tpecot/DeepLearningBasedSegmentationForBiologists


Deep Learning for image analysis - Exercises#

Martin Weigert

Licensed UNKNOWN

Tags: Fiji, Artificial Intelligence, Bioimage Analysis

Content type: Notebook

maweigert/zidas_2020_DL_intro_Part_2


Deep Vision and Graphics#

Victor Yurchenko, Fedor Ratnikov, Viktoriia Checkalina

Licensed MIT

Tags: Python, Artificial Intelligence

Content type: Notebook

yandexdataschool/deep_vision_and_graphics


DeepProfiler Handbook#

Michael Bornholdt, Juan Caicedo, Yu Han, Nikita Moshkov, Rebecca Senft

Licensed UNKNOWN

Tags: Artificial Intelligence, Bioimage Analysis

Content type: Book

cytomining/DeepProfiler-handbook

https://cytomining.github.io/DeepProfiler-handbook/docs/00-welcome.html


Dr Guillaume Jacquemet on studying cancer cell metastasis in the era of deep learning for microscopy#

Guillaume Jacquemet

Published 2024-10-24

Licensed UNKNOWN

Leukocyte extravasation is a critical component of the innate immune response, while circulating tumour cell extravasation is a crucial step in metastasis formation. Despite their importance, these extravasation mechanisms remain incompletely understood. In this talk, Guillaume Jacquemet presents a novel imaging framework that integrates microfluidics with high-speed, label-free imaging to study the arrest of pancreatic cancer cells (PDAC) on human endothelial layers under physiological flow conditions.

Tags: Artificial Intelligence, Bioimage Analysis

Content type: Video, Slides

https://www.youtube.com/watch?v=KTdZBgSCYJQ


EMBL Deep Learning course 2019 exercises and materials#

Valentyna Zinchenko, Pejman Rasti, Martin Weigert, Szymon Stoma

Licensed UNKNOWN

Tags: Python, Artificial Intelligence

Content type: Notebook

kreshuklab/teaching-dl-course-2019


EMBL Deep Learning course 2021/22 exercises and materials#

Martin Weigert, Constantin Pape

Licensed UNKNOWN

Tags: Python, Artificial Intelligence

Content type: Notebook

kreshuklab/teaching-dl-course-2022


EMBL Deep Learning course 2023 exercises and materials#

Martin Weigert, Uwe Schmidt, Benjamin Gallusser, Albert Dominguez Mantes, Buglakova Alyona

Licensed UNKNOWN

Tags: Python, Artificial Intelligence

Content type: Notebook

kreshuklab/teaching-dl-course-2023


FAIRy deep-learning for bioImage analysis#

Estibaliz Gómez de Mariscal

Licensed CC-BY-4.0

Introduction to FAIR deep learning. Furthermore, tools to deploy trained DL models (deepImageJ), easily train and evaluate them (ZeroCostDL4Mic and DeepBacs) ensure reproducibility (DL4MicEverywhere), and share this technology in an open-source and reproducible manner (BioImage Model Zoo) are introduced.

Tags: Artificial Intelligence, FAIR-Principles, Bioimage Analysis

Content type: Slides

https://f1000research.com/slides/13-147


Generative artificial intelligence for bio-image analysis#

Robert Haase

Licensed CC-BY-4.0

Tags: Python, Bioimage Analysis, Artificial Intelligence

Content type: Slides

https://f1000research.com/slides/12-971


Introduction to Deep Learning for Microscopy#

Costantin Pape

Licensed MIT

This course consists of lectures and exercises that teach the background of deep learning for image analysis and show applications to classification and segmentation analysis problems.

Tags: Artificial Intelligence, Python

Content type: Notebook

computational-cell-analytics/dl-for-micro


Kreshuk Lab’s EMBL EIPP predoc course teaching material#

Adrian Wolny, Johannes Hugger, Qin Yu, Buglakova Alyona

Licensed UNKNOWN

Tags: Artificial Intelligence

Content type: Tutorial

kreshuklab/predoc-course


Large Language Models: An Introduction for Life Scientists#

Robert Haase

Published 2024-12-12

Licensed CC-BY-4.0

This slide deck introduces Large Language Models to an audience of life-scientists. We first dive into terminology: Different kinds of Language Models and what they can be used for. The remaining slides are optional slides to allow us to dive deeper into topics such as tools for using LLMs in Science, Quality Assurance, Techniques such as Retrieval Augmented Generation and Prompt Engineering.

Tags: Globias, Artificial Intelligence

https://zenodo.org/records/14418209

https://doi.org/10.5281/zenodo.14418209


Machine Learning - Deep Learning. Applications to Bioimage Analysis#

Estibaliz Gómez-de-Mariscal

Licensed UNKNOWN

Tags: Artificial Intelligence, Bioimage Analysis

Content type: Slides

https://raw.githubusercontent.com/esgomezm/esgomezm.github.io/master/assets/pdf/SPAOM2018/MachineLearning_SPAOMworkshop_public.pdf


Machine and Deep Learning on the cloud: Segmentation#

Ignacio Arganda-Carreras

Licensed UNKNOWN

Tags: Neubias, Artificial Intelligence, Bioimage Analysis

Content type: Slides

https://docs.google.com/presentation/d/1oJoy9gHmUuSmUwCkPs_InJf_WZAzmLlUNvK1FUEB4PA/edit#slide=id.ge3a24e733b_0_54


MicroSam-Talks#

Constantin Pape

Published 2024-05-23

Licensed CC-BY-4.0

Talks about Segment Anything for Microscopy: computational-cell-analytics/micro-sam. Currently contains slides for two talks:

Overview of Segment Anythign for Microscopy given at the SWISSBIAS online meeting in April 2024 Talk about vision foundation models and Segment Anything for Microscopy given at Human Technopole as part of the EMBO Deep Learning Course in May 2024

Tags: Bioimage Analysis, Artificial Intelligence

Content type: Slides

https://zenodo.org/records/11265038

https://doi.org/10.5281/zenodo.11265038


Microscopy data analysis: machine learning and the BioImage Archive#

Andrii Iudin, Anna Foix-Romero, Anna Kreshuk, Awais Athar, Beth Cimini, Dominik Kutra, Estibalis Gomez de Mariscal, Frances Wong, Guillaume Jacquemet, Kedar Narayan, Martin Weigert, Nodar Gogoberidze, Osman Salih, Petr Walczysko, Ryan Conrad, Simone Weyend, Sriram Sundar Somasundharam, Suganya Sivagurunathan, Ugis Sarkans

Licensed CC-BY-4.0

The Microscopy data analysis: machine learning and the BioImage Archive course, which focused on introducing programmatic approaches used in the analysis of bioimage data via the BioImage Archive, ran in May 2023.

Tags: Bioimage Analysis, Python, Artificial Intelligence

Content type: Video, Slides

https://www.ebi.ac.uk/training/materials/microscopy-data-analysis-machine-learning-and-the-bioimage-archive-materials/


Neubias Academy 2020: Introduction to Nuclei Segmentation with StarDist#

Martin Weigert, Olivier Burri, Siân Culley, Uwe Schmidt

Licensed UNKNOWN

Tags: Python, Neubias, Artificial Intelligence, Bioimage Analysis

Content type: Slides, Notebook

maweigert/neubias_academy_stardist


NeubiasPasteur2023_AdvancedCellPose#

Gaelle Letort

Licensed BSD-3-CLAUSE

Tutorial for running CellPose advanced functions

Tags: Bioimage Analysis, Artificial Intelligence

Content type: Github Repository

gletort/NeubiasPasteur2023_AdvancedCellPose


Running Deep-Learning Scripts in the BiA-PoL Omero Server#

Marcelo Zoccoler

Licensed CC-BY-4.0

Tags: Python, Artificial Intelligence, Bioimage Analysis

Content type: Blog Post

https://biapol.github.io/blog/marcelo_zoccoler/omero_scripts/readme.html


Training Deep Learning Models for Vision - Compact Course#

Constantin Pape, Adrian Wolny

Licensed UNKNOWN

Tags: Artificial Intelligence, Bioimage Analysis

constantinpape/training-deep-learning-models-for-vison


ZIDAS 2020 Introduction to Deep Learning#

Estibaliz Gómez-de-Mariscal

Licensed UNKNOWN

Tags: Artificial Intelligence, Bioimage Analysis

Content type: Slides

esgomezm/zidas2020_intro_DL


ilastik: interactive machine learning for (bio)image analysis#

Anna Kreshuk, Dominik Kutra

Licensed CC-BY-4.0

Tags: Artificial Intelligence, Bioimage Analysis

Content type: Slides

https://zenodo.org/doi/10.5281/zenodo.4330625


introduction-to-generative-ai#

Bruna Piereck, Alexander Botzki

Published 2024-09-27T14:38:51+00:00

Licensed CC-BY-4.0

Course repository for Strategic Use of Generative AI

Tags: Artificial Intelligence

Content type: Github Repository, Tutorial

vibbits/introduction-to-generative-ai

https://liascript.github.io/course/?https://raw.githubusercontent.com/vibbits/introduction-to-generative-ai/refs/heads/main/README.md