Lab meeting
10-03-2020
Lab meeting
10-03-2020
29th - 2nd
3rd
4th - 6th
Training schools
- Early career Invesitigators
- Bioimage Analysts
Taggathon
Satellite
Meeting
Symposium
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Centre Broca Nouvelle-Aquitaine
Notes from the conference:
Taggathon
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Sattelite
meeting
Sattelite meeting
- Machine learning for Microscopy
- Bioimage Analysis Facility Management
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- Uses the Tensorflow Java API
- Easily run pre-trained models on ImageJ
- A community platform for models as if they were plugins
- Macro recordable
- CPU and GPU compatible
- Good way to make your network accessible to biologists
GPU cluster at Pasteur
- Contact: Dmitry Ershov
https://research.pasteur.fr/en/member/dmitry-ershov/ - They use Slurm as workload manager.
https://slurm.schedmd.com/
Maybe it has better GPU support? - Discussion about training in loop to find the best network architechture/parameters
Ilastik
https://www.ilastik.org/
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- Pixel classifier with friendly interface
- Easy input from users
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Supports
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Pixel classification
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Object classification
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Carving (segmentation in 3D)
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Boundary based segmentation (Multicut)
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Tracking
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Now supports DL models
Pre-trained networks with fine-tuning from user input
- 3 facilities joined the discussion
Porto, Nantes and Zurich - 3 different approaches:
Free, Almost free, Expensive - Good discussion, it's important feedback for our Image Facility
Bioimage analysis Facility management
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Symposium
QuPath
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For histopathology data, huge images (60Gb) that are represented in different resolutions.
(google maps style)
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Cell detection and feature measurement.
Has a classifier that can learn from user input annotation.
- From the public feedback, it’s widely used.
Piximi
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Open source app for object recognition, from the Broad institute and Carpenter lab
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It’s a deep learning cell classifier on a web application (piximi.app)
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The idea is to replace CellProfiler
- Really nice interface
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Panel discussion
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F1000 Research
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Immediate publication (after editorial review) and actual peer-review post-publication
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There’s a clear label on the paper during this waiting peer review period
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Lots of different categories to publish
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Open peer review (reviewers always named)
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Report of reviewers is accessible
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Peer review for the technical aspect, not the impact of research (PLoS like)
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All versions of the paper are accessible and citable
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Interactive figures with Plotly or Shiny apps
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Readers get notified if a new version of the paper they downloaded is available
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F1000 | Nature Methods | Nature Comm |
Scientific Reports | PLoS One |
PLoS Comp Bio |
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2.64 | 23.03 | 11.80 | 4.12 | 2.87 | 4.38 |
Emma Lundberg
One of the responsibles for The human protein atlas and also the organization of the Kaggle competition for cell classification, that's finished and published now.
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How to get ground truth for millions of images?
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32 million classifications
70 working years
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CLIJ - GPU support for Fiji
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Spot detection example:
2h40 hours on CPU
11min on GPU
Kristin Branson
- Deep learning model for tracking flies in videos
- Automated system analysed 400 000 flies
- All the acquired data allowed to create another DL model that simulates the social behavior fo synthetic flies
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Conclusions
- Everything is Deep learning now. More than 90% of the image analysis conference was about DL.
- People talked a lot about a "Model Zoo". A single place where DL trained models can be downloaded. Tensor flow is, without doubt, the standard.
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image.sc is building strength, lots of developers are talking about it.
- Artificial Labeling is getting mainstream, especially with the industry software (Nikon, Zeiss, etc)
thanks!
Lab meeting
By Felipe Delestro
Lab meeting
- 780