Continual-Zoo: Leveraging Zoo Models for Continual Classification of Medical Images
Bibliographic record
Abstract
In medical imaging, leveraging continual learning (CL) is key for models to adapt to new classes and data distributions without forgetting prior knowledge. Existing CL methods often overlook the use of off-the-shelf pretrained models that are equipped with informative and generalizable representations, opting instead to learn from scratch. In this paper, we propose Continual-Zoo, a novel CL paradigm that smartly leverages a zoo of pretrained models for continual medical image classification. For a given task, Continual-Zoo distills pertinent knowledge from the fixed zoo through cross-knowledge and semantic-knowledge attention mechanisms to obtain class prototypes. Since deploying a zoo could lead to scalability issues with a large number of models, we propose a novel prototypical variational autoencoder, pVAE, as a zoo knowledge encoder. During inference, Continual-Zoo utilizes pVAE as a feature extractor that maps images to the same space of class prototypes and returns the class whose prototype has the shortest distance in the latent space. To mitigate forgetting in CL, pVAE leverages the class prototypes to synthesize images from previously learned tasks before adapting to new ones. Experimental results on various clinical benchmarks demonstrate the superiority of Continual-Zoo over SOTA methods in class-incremental, domain-incremental, and domain and class-incremental learning scenarios, distinguishing it from most CL methods.Code is available at here.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".