Model card
MVS-GCN.
Researchopen-sourceMulti-view Site Graph Convolutional Network
Handles multi-site variability. 69.38% accuracy on ABIDE dataset.
§ 02 · Benchmarks
Every benchmark MVS-GCN has a recorded score for.
| # | Benchmark | Area · Task | Metric | Value | Rank | Date | Source |
|---|---|---|---|---|---|---|---|
| 01 | ABIDE I | Medical · Disease Classification | auc | 69.0% | #9 | — | source ↗ |
| 02 | ABIDE I | Medical · Disease Classification | accuracy | 69.4% | #22 | — | source ↗ |
Rank column shows this model’s position vs all other models scored on the same benchmark + metric (competitors after the slash). #1 in red means current SOTA. Sorted by rank, then newest result.
§ 05 · Related models
Other Research models scored on Codesota.
§ 06 · Sources & freshness
Where these numbers come from.
research-paper
2
results
0 of 2 rows marked verified.