When evidence modeling meets knowledge distillation: Towards reliable contrast-enhanced knowledge distillation for non-contrast medical image segmentation
Bibliographic record
Abstract
Contrast-enhanced knowledge distillation promises to transform medical diagnostics and reveal promising approaches for tumor segmentation on non-contrast medical images. However, existing methods related to contrast-enhanced knowledge distillation still make it hard to distill reliable contrast-enhanced knowledge for tumor segmentation due to the limitations of (1) unable to quantify uncertainty information for reliable contrast-enhanced and non-contrast knowledge modeling, which leads to an over-confidence cross-domain adaptation for transferring contrast-enhanced knowledge; (2) using vision information only ignores rich semantic features in medical language, which make it hard to model complex tumor enhancement feature. In this study, we propose an evidence-guided and tumor-aware knowledge distillation (EGTA-KD) for transferring contrast-enhanced domain knowledge to non-contrast domain knowledge. Specifically, to achieve tumor-awareness by embedding semantic features from text, the tumor-aware cross-modal synchronizer (TACMS) is proposed to calculate tumor score maps for matching pixel wise image and text features. To achieve reliable cross-domain modeling for transferring contrast-enhanced knowledge, the innovative uncertainty-quantified evidence unit (UQEU) parameterizes the probability distribution within subjective logic to gather reliable evidence of contrast-enhanced knowledge while quantifying the uncertainty of prediction. Lastly, newly designed dual-level knowledge distillation (DLKD) minimizes tumor score map errors and matches evidence distribution for uncertainty-aware contrast-enhanced knowledge distillation. Extensive experiments of tumor segmentation on non-contrast medical images are performed using multi-modality medical image datasets (i.e., Brain MRI dataset, Liver MRI dataset, and Kidney CT dataset). Experimental results demonstrate the proposed EGTA-KD outperforms the other compared state-of-the-art methods, revealing its superiority of tumor segmentation on non-contrast medical images via uncertainty-aware contrast-enhanced knowledge distillation.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".