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 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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".