Towards Domain-Aware Transfer Learning for Medical Image Analysis: Opportunities and Challenges
Bibliographic record
Abstract
The tremendous success of transfer learning (TL) in natural imaging has also motivated the researchers in biomedical imaging.A lot of methods utilizing TL have been proposed, however, only a few have emphasized on its actual impact in biomedical tasks.In this article, we review the current landscape of TL in medical image analysis, and outlined the existing myths and related findings.We found that there exists substantial lack of medically specialized (domain-specific) pretrained transfer learning models, which can significantly benefit the biomedical imaging.Thus, to further explore our opinion experimentally, we identified three large datasets previously available from different medical areas and pretrained the standard CNN models on them, both separately and on aggregated dataset.These pre-trained models are then transferred for five different target medical tasks and their performance is compared.The comparison has shown promising benefits of domain-aware learning and aggregated generalized medical TL models along with associated challenges.We believe the outcomes of this work will encourage the community to rethink the existing de-facto ImageNet TL standard, and work for the domain-specific TL.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".