TY - GEN
T1 - Patch-Level Glioblastoma Subregion Classification with a Contrastive Learning-Based Encoder
AU - Zhang, Juexin
AU - Zhong, Qifeng
AU - Weng, Ying
AU - Chen, Ke
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.
PY - 2026/4
Y1 - 2026/4
N2 - The significant molecular and pathological heterogeneity of glioblastoma, an aggressive brain tumor, complicates diagnosis and patient stratification. While traditional histopathological assessment remains the standard, deep learning offers a promising path toward objective and automated analysis of whole slide images. For the BraTS-Path 2025 Challenge, we developed a method that fine-tunes a pre-trained Vision Transformer (ViT) encoder with a dedicated classification head on the official training dataset. Our model’s performance on the online validation set, evaluated via the Synapse platform, yielded a Matthews Correlation Coefficient (MCC) of 0.7064 and an F1-score of 0.7676. On the final test set, the model achieved an MCC of 0.6509 and an F1-score of 0.5330, which secured our team second place in the BraTS-Pathology 2025 Challenge. Our results establish a solid baseline for ViT-based histopathological analysis, and future efforts will focus on bridging the performance gap observed on the unseen validation data.
AB - The significant molecular and pathological heterogeneity of glioblastoma, an aggressive brain tumor, complicates diagnosis and patient stratification. While traditional histopathological assessment remains the standard, deep learning offers a promising path toward objective and automated analysis of whole slide images. For the BraTS-Path 2025 Challenge, we developed a method that fine-tunes a pre-trained Vision Transformer (ViT) encoder with a dedicated classification head on the official training dataset. Our model’s performance on the online validation set, evaluated via the Synapse platform, yielded a Matthews Correlation Coefficient (MCC) of 0.7064 and an F1-score of 0.7676. On the final test set, the model achieved an MCC of 0.6509 and an F1-score of 0.5330, which secured our team second place in the BraTS-Pathology 2025 Challenge. Our results establish a solid baseline for ViT-based histopathological analysis, and future efforts will focus on bridging the performance gap observed on the unseen validation data.
KW - BraTS 2025
KW - Deep learning
KW - Digital Pathology
KW - Glioblastoma
UR - https://www.scopus.com/pages/publications/105037993158
U2 - 10.1007/978-3-032-16370-7_17
DO - 10.1007/978-3-032-16370-7_17
M3 - Conference contribution
AN - SCOPUS:105037993158
SN - 9783032163691
T3 - Lecture Notes in Computer Science
SP - 193
EP - 202
BT - Segmentation, Classification, and Synthesis for Brain Tumors and Traumatic Brain Injuries - MICCAI 2025 Challenges
A2 - Bakas, Spyridon
A2 - Dennis, Emily
A2 - Astaraki, Mehdi
A2 - Baid, Ujjwal
A2 - Conte, Gian Marco
A2 - Foltyn-Dumitru, Martha
A2 - Jiang, Zhifan
A2 - Linguraru, Marius George
A2 - Labella, Dominic
A2 - Metz, Marie-Christin
A2 - Anazodo, Udunna
A2 - de Verdier, Maria Correia
A2 - Kofler, Florian
A2 - Li, Hongwei Bran
A2 - Maleki, Nazanin
PB - Springer Science and Business Media Deutschland GmbH
T2 - 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
Y2 - 23 September 2025 through 27 September 2025
ER -