A meta-learning-based robust federated learning for diagnosing lung adenocarcinoma and tuberculosis granulomas
Bibliographic record
Abstract
Background: Differentiating between lung adenocarcinoma (LAC) and tuberculosis granuloma (TBG) of solitary pulmonary solid nodules (SPSNs) based on CT images alone is a daunting task for clinical diagnosis. Thus, it is crucial to fully utilize CT imaging data to explore effective noninvasive diagnostic methods to improve the identification of TBG and LAC. Purpose: This study aimed to leverage CT imaging datasets from multiple hospitals for the diagnosis of TBG and LAC in SPSNs. It achieved this by deploying a meta-learning method within a federated learning framework while protecting data privacy. Methods: A total of 1,026 patients, along with their CT images of solitary pulmonary solid nodules (SPSNs) and corresponding clinical data, were collected from six medical institutions. Subsequently, the data from these six institutions were systematically partitioned into five cohorts. Each cohort was divided into two parts: the training set and the test set. A meta-learning-based robust federated learning model by training set data was proposed to construct personalized federated learning signatures (PFLS) without uploading raw data from each medical institutions. Receiver operating characteristic curve (ROC), area under curve (AUC), decision curve analysis (DCA), net reclassification improvement (NRI) and integrated discrimination improvement (IDI) are used to analyze the performance of the PFLS. Results: The PFLS trained by the proposed meta-learning-based robust federated learning framework shows superior performance compared to alternative methods. The AUC range on the training sets of the five cohorts is 0.866-0.939, AUC range on the testing sets is 0.808-0.927). The significant difference of AUC between the proposed method and the clinical model was demonstrated by the NRI and IDI. The decision curves indicated a higher net benefit of our proposed method. Conclusion: The PFLS mitigates overfitting issues arising from limited sample size in local hospitals. It also alleviates the problem that a single global model is not applicable to all hospitals due to the heterogeneity of data distribution among different hospitals.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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 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".