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When the pen is mightier than the sword: semi-automatic 2 and 3D image labelling

Hollandi, R.; Bauer, D.; Diosdi, A.; Schrettner, B.; Toth, T.; Hirling, D.; Hollandi, G.; Harmati, M.; Molnar, J.; Horvath, P.

2024-01-16 bioinformatics
10.1101/2024.01.15.575658 bioRxiv
Show abstract

Data is the driving engine of learning-based algorithms, the creation of which fundamentally determines the performance, accuracy, generalizability and quality of any model or method trained on it. When only skilled or trained personnel can create reliable annotations, assisted software solutions are desirable to reduce the time and effort the expert must spend on labelling. Herein is proposed an automated annotation helper software package in napari that offers multiple methods to assist the annotator in creating object-based labels on 2D or 3D images.

Matching journals

The top 5 journals account for 50% of the predicted probability mass.

1
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Top 0.6%
30.8%
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50% of probability mass above
6
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Top 9%
3.4%
7
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Top 3%
3.2%
8
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○ 993 papers in training set
Top 6%
3.2%
9
BMC Bioinformatics
○ 457 papers in training set
Top 3%
3.1%
10
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◐ 212 papers in training set
Top 1%
2.8%
11
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Top 0.2%
2.4%
12
Journal of Cell Science
● 393 papers in training set
Top 2%
2.4%
13
BMC Biology
○ 265 papers in training set
Top 1%
2.1%
14
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○ 165 papers in training set
Top 1%
1.9%
15
PLOS ONE
● 5266 papers in training set
Top 46%
1.7%
16
Small Methods
○ 29 papers in training set
Top 0.3%
1.7%
17
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● 285 papers in training set
Top 4%
1.5%
18
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◐ 203 papers in training set
Top 4%
1.3%
19
Computational and Structural Biotechnology Journal
● 242 papers in training set
Top 5%
1.1%
20
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◐ 242 papers in training set
Top 4%
1.0%
21
Frontiers in Bioengineering and Biotechnology
○ 98 papers in training set
Top 2%
0.8%
22
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○ 33 papers in training set
Top 0.3%
0.8%
23
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○ 20 papers in training set
Top 0.2%
0.8%
24
Advanced Intelligent Systems
○ 11 papers in training set
Top 0.3%
0.8%
25
npj Imaging
○ 12 papers in training set
Top 0.2%
0.8%
26
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○ 78 papers in training set
Top 3%
0.8%
27
eLife
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Top 71%
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"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.