Attention-Based Medical Knowledge Injection in Deep Image Classification Models
Bibliographic record
Abstract
Deep learning for medical image classification is extremely important for decision support in medical healthcare settings. General-purpose neural network architectures for image classification have become increasingly sophisticated in recent years. Among them, attention-based models have provided significant advancements in deep learning. However, attention mechanisms adopted thus far focus on minimizing task-specific losses and do not fruitfully exploit medical knowledge, such as lesion-specific characteristics, during the training process, resulting in a potential reduction in accuracy. In this paper, we propose an attention-based approach that leverages knowledge of the localization of specific lesion types to guide the model training process. To this end, gradient-based activation mapping is used for incentivizing models to focus on the right area for a given lesion type. The approach is general since it can be applied to any deep learning architecture in end-to-end model training. Our experiments on two real-world medical image datasets show the ability of our approach to improve the classification performance of popular deep-learning model architectures over the classical cross-entropy loss.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".