PathViT Model for Automated Disease Classification from Skeletal Muscle Histopathology
Bibliographic record
Abstract
Analyzing skeletal muscle pathology from histological images is labor intensive (requiring manual cell counting, segmentation, and thresholding), time consuming, and prone to inter- and intrauser variability, influencing the accuracy and consistency of diagnoses. To address these difficulties, PathViT, a transformer-based deep-learning model, was designed to automatically distinguish between healthy and diseased muscle fibers, with the aims of reducing human intervention, minimizing subjectivity and variability, and significantly decreasing analysis time compared to conventional manual methods. Skeletal muscle pathology is characterized by changes in myofiber cross-sectional area, increased central nuclei, and structural disruptions in sarcomeres. To investigate these changes in myofiber size, wheat germ agglutinin staining and digital histopathology of skeletal muscle (quadriceps, gastrocnemius, tibialis anterior, extensor digitorum longus, and soleus) was utilized to classify diseased tissue [amyotrophic lateral sclerosis (SOD1∗G93A) and type 1 diabetes (Akita)] versus nondiseased controls. The performance of PathViT in distinguishing diseased versus nondiseased muscle fibers was compared with that of state-of-the-art deep-learning models. PathViT classified healthy and diseased muscle fibers with 96% accuracy, outperforming the other models. This approach enhanced scalability and diagnostic accuracy and decreased variability, making PathViT a potentially powerful biomedical research and clinical tool.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".