Szczegóły publikacji
Opis bibliograficzny
Spatially refined transformer embeddings for accurate histopathology tissue classification / Przemysław NIEDZIELA, Bogusław CYGANEK // W: Computational Science – ICCS 2026 workshops : 26th International Conference : Hamburg, Germany, June 29–July 1, 2026 : proceedings , Pt. 2 / eds. Maciej Paszynski, Amanda S. Barnard, Yongjie Jessica Zhang. — Cham : Springer, cop. 2026. — ( Lecture Notes in Computer Science ; ISSN 0302-9743 ; LNCS 16787 ). — ISBN: 978-3-032-29908-6; e-ISBN: 978-3-032-29909-3. — S. 428–442. — Bibliogr., Abstr. — Publikacja dostępna online od: 2026-06-28
Autorzy (2)
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 168917 |
|---|---|
| Data dodania do BaDAP | 2026-08-31 |
| DOI | 10.1007/978-3-032-29909-3_31 |
| Rok publikacji | 2026 |
| Typ publikacji | materiały konferencyjne (aut.) |
| Otwarty dostęp |  |
| Wydawca | Springer |
| Konferencja | International Conference on Computational Science 2026 |
| Czasopismo/seria | Lecture Notes in Computer Science |
Abstract
Accurate recognition of tissue types in histopathological whole-slide images is a fundamental challenge in computational pathology, particularly given the global decline in practicing histopathologists. We present a classification framework that integrates a lightweight Vision Transformer (TinyViT) for patch-level feature extraction with dimensionality reduction via Principal Component Analysis and Neighborhood Component Analysis. The resulting low-dimensional yet discriminative representations are classified using k-Nearest Neighbors and Support Vector Machines, yielding state-of-the-art performance on the DiagSet prostate cancer dataset and on BreakHis, even under extreme reductions in feature dimensionality. To further improve robustness, we introduce a spatial refinement strategy that projects patch predictions into a grid representation of the slide, enforcing spatial consistency by identifying and reclassifying low- and high-confidence regions. This two-stage process enhances predictive accuracy and improves interpretability by highlighting confident as well as uncertain tissue areas. On DiagSet, our framework achieves 82.70% in the 4-class and over 90.6% in the binary setting, surpassing prior baselines, while post-hoc spatial refinement yields consistent gains of up to 2% points without retraining.
