FCAformer: Fuzzy-Enhanced Class-Aware Attention Based Transformer for Weakly Supervised Histopathology Image Segmentation
Bibliographic record
Abstract
Pixel-level histopathology image segmentation plays a vital role in computational pathology, and weakly supervised segmentation methods, which rely solely on image-level labels, have shown great potential. However, most existing weakly supervised segmentation methods are limited by the fixed receptive field of convolutional neural networks, and overlook the uncertainty of the distribution of different tissue types and the fuzziness of class boundaries, resulting in limited segmentation effects. To address these problems, we propose a fuzzy-enhanced class-aware attention based Transformer (FCAformer) for weakly supervised histopathology image segmentation. FCAformer employs the Transformer architecture for model global contextual information, which effectively alleviates the limitation of fixed receptive field on the size of attention map in traditional methods. Subsequently, FCAformer integrates fuzzy system to model the uncertainty in histopathology images. Specifically, it assigns membership function to the output feature map of the last layer of Transformer encoder, generates fuzzy membership matrix, extracts fuzzy features by combining three fuzzy rules, and finally fuses these features to generate fuzzy attention map. This attention map guides the network to learn the characteristics of different tissue types and improve the fuzziness of class boundaries, thereby improving the modeling ability of the model on uncertain tissue distribution and fuzzy areas. In addition, based on the idea of contrastive learning, we design contrastive class token loss to further enhance the distinguishability between different class labels. Extensive experiments on LUAD-HistoSeg and BCSS-WSSS datasets demonstrate that FCAformer achieves state-of-the-art segmentation performance in weakly supervised segmentation tasks.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".