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

Research output: Book / Anthology / Report / Ph.D. thesisReportResearchpeer-review

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. ...
Original languageEnglish
Number of pages11
DOIs
Publication statusPublished - 2024

Keywords

  • Transfer learning
  • Medical imaging
  • Shortcut learning
  • Image statistics

Fingerprint

Dive into the research topics of 'Exploring connections of spectral analysis and transfer learning in medical imaging ...'. Together they form a unique fingerprint.

Cite this