Monroy LCR, Mayr M, Mill L, Köstler H, Maier A (2026)
Publication Type: Conference contribution
Publication year: 2026
Publisher: Springer Science and Business Media Deutschland GmbH
Book Volume: 16377 LNCS
Pages Range: 213-222
Conference Proceedings Title: Lecture Notes in Computer Science
Event location: Daejeon, KOR
ISBN: 9783032163691
DOI: 10.1007/978-3-032-16370-7_19
Accurate patch-level classification of glioblastoma sub-regions is essential for diagnosis but challenged by tumor heterogeneity and extreme class imbalance. We propose an ensemble framework that uses feature embeddings from four foundation models: UNI, Virchow, Gigapath and Midnight. Each embedding is partitioned into fixed-size chunks to preserve morphological detail and enable localized modeling. Dedicated XGBoost classifiers are trained on each chunk using balanced sample weights to mitigate class imbalance. A shared chunk selection mechanism ensures consistency between training and validation sets and prevents data leakage. Chunk-level predictions are fused via simple averaging into a unified probability vector. To enhance detection of rare sub-regions we apply rare-class boosting by scaling their predicted probabilities before re-normalization. Per-class decision thresholds are optimized on the validation set to maximize macro-F1 score improving sensitivity to underrepresented patterns. The pipeline achieves a global-averaged F1 score of 0.76 and a Matthews correlation coefficient of 0.70 demonstrating robustness under domain shift and severe label imbalance. Our approach highlights the value of feature chunking ensemble fusion and adaptive post-processing in integrating foundation models for digital pathology. The method provides a reproducible and effective solution for sub-region analysis in glioblastoma histology.
APA:
Monroy, L.C.R., Mayr, M., Mill, L., Köstler, H., & Maier, A. (2026). Patch-Level Brain Tumor Sub-region Classification Using Foundation Models Under Long-Tailed Data Distributions. In Spyridon Bakas, Emily Dennis, Mehdi Astaraki, Ujjwal Baid, Gian Marco Conte, Martha Foltyn-Dumitru, Zhifan Jiang, Marius George Linguraru, Dominic Labella, Marie-Christin Metz, Udunna Anazodo, Maria Correia de Verdier, Florian Kofler, Hongwei Bran Li, Nazanin Maleki (Eds.), Lecture Notes in Computer Science (pp. 213-222). Daejeon, KOR: Springer Science and Business Media Deutschland GmbH.
MLA:
Monroy, Luis Carlos Rivera, et al. "Patch-Level Brain Tumor Sub-region Classification Using Foundation Models Under Long-Tailed Data Distributions." Proceedings of the Brain TumorS Lighthouse Cluster of Challenges, and the Automated Identification of Moderate-Severe Traumatic Brain Injury Lesions Challenge, BraTS 2025 and AIMS-TBI 2025, held in Conjunction International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2025, Daejeon, KOR Ed. Spyridon Bakas, Emily Dennis, Mehdi Astaraki, Ujjwal Baid, Gian Marco Conte, Martha Foltyn-Dumitru, Zhifan Jiang, Marius George Linguraru, Dominic Labella, Marie-Christin Metz, Udunna Anazodo, Maria Correia de Verdier, Florian Kofler, Hongwei Bran Li, Nazanin Maleki, Springer Science and Business Media Deutschland GmbH, 2026. 213-222.
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