TexSegNet: An Attention-Guided Feedback-Driven Texture-Aware Deep Learning Model for Nuclei Segmentation and Classification in Digital Pathology Images of Breast Tissues*
Bibliographic record
Abstract
Accurate nuclei instance segmentation and classification play a crucial role in computational pathology, particularly for breast cancer diagnosis and characterization. However, existing methods often struggle with relatively high false positive/negative rates in detecting nuclei, inadequate nuclei texture representation leading to misclassification of nucleus types, and difficulties in properly segmenting clustered or touching nuclei in digital pathology images. In this paper, we propose TexSegNet, a hybrid encoder-decoder model that integrates multi-scale convolutions, nuclear texture extraction blocks, advanced attention mechanisms, and a feedback-driven classification branch. Trained on all tissue types included in the PanNuke dataset and subsequently fine-tuned on its breast subset, TexSegNet achieves over 4% higher accuracy in detecting and classifying nuclei on the breast test set as compared to competing models such as CellViT. Notably, TexSegNet maintains very good performance across various cell types, including underrepresented ones, with F1-scores of 89.3 ± 0.4%, 91.1 ± 0.5%, 88.9 ± 0.8%, and 84.3 ± 0.3% in detecting and classifying neoplastic, epithelial, inflammatory, and connective cell nuclei, respectively. These findings underscore TexSegNet's potential as a reliable tool for digital pathology research and as a decision-support tool to enhance diagnostic accuracy in breast histopathology.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".