Evidence map›Paper›PMID 40575597›Full record

ArticleProceedings of SPIE--the International Society for Optical Engineering2025

Exploring connections of spectral analysis and transfer learning in medical imaging.

Yucheng Lu, Dovile Juodelyte, Jonathan D Victor, Veronika Cheplygina

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Proceedings of SPIE--the International Society for Optical Engineering, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Yucheng LuIT University of Copenhagen, Rued Langgaards Vej 7, Copenhagen, Denmark.
Dovile JuodelyteIT University of Copenhagen, Rued Langgaards Vej 7, Copenhagen, Denmark.
Jonathan D VictorWeill Cornell Medicine, 1300 York Avenue, New York, USA.
Veronika CheplyginaIT University of Copenhagen, Rued Langgaards Vej 7, Copenhagen, Denmark.

Funding

CENTRAL PROCESSING OF VISUAL INFORMATIONR01EY007977 · NEI · WEILL MEDICAL COLL OF CORNELL UNIV · PI Jonathan D Victor · 1989 to 2026
$9.0M
NEI NIH HHS R01 EY007977
6 · The paper itself

Abstract

In this paper, we use spectral analysis to investigate transfer learning and study model sensitivity to frequency shortcuts in medical imaging. By analyzing the power spectrum density of both pre-trained and fine-tuned model gradients, as well as artificially generated frequency shortcuts, we observe notable differences in learning priorities between models pre-trained on natural vs medical images, which generally persist during fine-tuning. We find that when a model's learning priority aligns with the power spectrum density of an artifact, it results in overfitting to that artifact. Based on these observations, we show that source data editing can alter the model's resistance to shortcut learning. Code available at: https://github.com/YCL92/Shortcut-PSD.

Indexed as

Image statisticsMedical imagingShortcut learningTransfer learning

Identifiers

PMID40575597
PMCPMC12201968

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.