Bridging Classification and Localization in X-Ray Fracture Diagnosis via Weakly-Supervised Vision Transformers
Bibliographic record
Abstract
Bone fracture diagnosis from radiographs is a crucial yet time-consuming job requiring professional interpretation. Typical fully supervised learning methods require expensive and often scarce annotation data, especially in medical imaging. We propose a weakly supervised learning (WSL) approach for automatic fracture classification that mimics the holistic strategy of human radiologist. In our method, Vision Transformer (ViT) models (including Base, Large, distillation, and self-supervised variants) are trained using only image-level labels without any bounding box annotations. Despite the absence of localized supervision, our WSL ViT-based models achieve high fracture detection accuracy on the FracAtlas dataset of musculoskeletal X-rays. More importantly, the learned self-attention maps provide human interpretable heatmaps highlighting suspect regions, effectively bridging classification and localization. We demonstrate that our approach detects fractures like a human radiologist, scanning the entire image and focusing on abnormal patterns through learned attention. Experimental results showed that our WSL ViT-based architecture outperforms recent CNN-based methods in classification accuracy while also producing reliable visual explanations. Our best-performing model, a distilled ViT (DeiT) variant, achieved 94% classification accuracy with ROC AUC of 0.93. This work establishes a promising step toward accurate and interpretable fracture detection with minimal supervision. Potentially reducing the need for expensive localization annotations and aiding clinical decision-making.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".