Exploring a Universal Training Method for Medical Image Classification
Bibliographic record
Abstract
In recent years, with the application of deep learning technology in the field of medical image analysis, computer-aided medical image classification can help doctors diagnose and treat patients better. However, due to the particularity of medical images, the performance of traditional image processing is not satisfactory to all medical images. Self-supervised pretraining followed by supervised finetuning has seen success in image recognition, but has received limited attention in medical image classification. In this paper, we propose a method based on self-supervised pretraining and supervised finetuning. In the pretraining step, we train our backbone on unlabeled ImageNet and MedMNIST to learn different types of image features. In the finetuning step, we carefully compare our training method in two modalities with several mainstream methods. Our pretraining method outperforms supervised baselines pretrained on ImageNet. In addition, we show that with suitable pretraining method adopted, our proposed method could be reused on several similar tasks with little modification.
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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) | 0.003 | 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".