Interactive Open-source Image Analysis and Scientific Plotting Workshop
Bibliographic record
Abstract
In recent years, increasingly sophisticated Python tools have become available for otherwise tedious, manual image analysis tasks. At the same time, it is becoming increasingly difficult for non-specialized Python-programmers to gain access to these methods, as there is often little standardization and interoperability between projects. In that circumstance, napari has taken the stage, offering advanced multidimensional image data visualization and simple creation of graphical user interfaces for image analysis workflows. Our workshop aims to make end users familiar with the napari viewer itself and its plugin ecosystem. This includes a tour through napari's visualization layers as well as a collection of interoperable tools for image machine-learning-driven segmentation, feature extraction and unsupervised-machine learning methods for feature exploration. More specifically, we will show how to open n-dimensional data in napari, how to perform semantic and instance segmentation, how to extract features from objects in different channels, how to use these features to classify these objects, and finally how to save and plot these results with open-source libraries/software. The workshop aims at life scientists and facility staff members who wish to improve their proficiency in image visualization, image analysis and graph plotting with napari plugins and other open-source tools. Our workshop will make attendants more confident and proficient in developing modern image analysis workflows using open-source tools. Knowledge in Python is recommended, but not required.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.007 | 0.009 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.178 | 0.118 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".