You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output.
Click here to find out more.
X Demographics
Mendeley readers
Attention Score in Context
| Chapter title |
Anomaly Detection Using Generative Models and Sum-Product Networks in Mammography Scans
|
|---|---|
| Chapter number | 8 |
| Book title |
Deep Generative Models
|
| Published in |
arXiv, October 2022
|
| DOI | 10.1007/978-3-031-18576-2_8 |
| Book ISBNs |
978-3-03-118575-5, 978-3-03-118576-2
|
| Authors |
Marc Dietrichstein, David Major, Martin Trapp, Maria Wimmer, Dimitrios Lenis, Philip Winter, Astrid Berg, Theresa Neubauer, Katja Bühler, Dietrichstein, Marc, Major, David, Trapp, Martin, Wimmer, Maria, Lenis, Dimitrios, Winter, Philip, Berg, Astrid, Neubauer, Theresa, Bühler, Katja |
X Demographics
The data shown below were collected from the profiles of 7 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Austria | 2 | 29% |
| Japan | 1 | 14% |
| Unknown | 4 | 57% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 6 | 86% |
| Science communicators (journalists, bloggers, editors) | 1 | 14% |
Mendeley readers
The data shown below were compiled from readership statistics for 6 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 6 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Master | 3 | 50% |
| Other | 1 | 17% |
| Researcher | 1 | 17% |
| Unknown | 1 | 17% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 3 | 50% |
| Arts and Humanities | 1 | 17% |
| Engineering | 1 | 17% |
| Unknown | 1 | 17% |
Attention Score in Context
This research output has an Altmetric Attention Score of 4. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 06 February 2023.
All research outputs
#7,241,829
of 24,093,053 outputs
Outputs from arXiv
#151,094
of 1,018,817 outputs
Outputs of similar age
#132,721
of 426,533 outputs
Outputs of similar age from arXiv
#6,488
of 39,950 outputs
Altmetric has tracked 24,093,053 research outputs across all sources so far. This one has received more attention than most of these and is in the 69th percentile.
So far Altmetric has tracked 1,018,817 research outputs from this source. They receive a mean Attention Score of 4.0. This one has done well, scoring higher than 84% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 426,533 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 68% of its contemporaries.
We're also able to compare this research output to 39,950 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 83% of its contemporaries.