Improved Breast Cancer Classification Using Prior Information and Decentralized Training Approach
Bibliographic record
Abstract
Breast ultrasound (US) datasets are often small in size and contain tumors of varying sizes and shapes that make classification of breast US images as benign or malignant nontrivial, and learning and generalization of such classification models challenging. This challenge can be alleviated to some extent by incorporating prior information, such as breast tumour masks. However, tumour masks are not available for all US datasets. This paper proposes a novel method that adopts a decentralized training procedure inspired by federated learning, where a server provides the backbone to two clients which train local models using their own datasets. One of the clients uses the prior information for its learning and transfers this information via the weights of the updated backbone to the second client (through the server) which only uses US images for training its head and the backbone. This novel method outperforms other training approaches, including training from scratch, transfer learning, and knowledge distillation, by at least 3% and 8% in terms of accuracy and Matthews correlation coefficient, respectively.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| 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".