A4 Artikkeli konferenssijulkaisussa
Self-supervised Contrastive Pre-training Integrated with Multi-level Co-attention for Survival Prognosis from Whole Slide Images (2024)


Gao, J., Hao, X., Jin, S., & Xu, H. (2024). Self-supervised Contrastive Pre-training Integrated with Multi-level Co-attention for Survival Prognosis from Whole Slide Images. In P. You, S. Liu, & J. Wang (Eds.), Proceedings of International Conference on Image, Vision and Intelligent Systems 2023 (ICIVIS 2023) (pp. 650-658). Springer. Lecture Notes in Electrical Engineering, 1163. https://doi.org/10.1007/978-981-97-0855-0_62


JYU-tekijät tai -toimittajat


Julkaisun tiedot

Julkaisun kaikki tekijät tai toimittajatGao, Junxiu; Hao, Xinyu; Jin, Shan; Xu, Hongming

EmojulkaisuProceedings of International Conference on Image, Vision and Intelligent Systems 2023 (ICIVIS 2023)

Emojulkaisun toimittajatYou, Peng; Liu, Shuaiqi; Wang, Jun

Konferenssi:

  • International Conference on Image, Vision and Intelligent Systems

Konferenssin paikka ja aikaBaoding, China16.-18.8.2023

ISBN978-981-97-0854-3

eISBN978-981-97-0855-0

Lehti tai sarjaLecture Notes in Electrical Engineering

ISSN1876-1100

eISSN1876-1119

Julkaisuvuosi2024

Ilmestymispäivä25.02.2024

Sarjan numero1163

Artikkelin sivunumerot650-658

Kirjan kokonaissivumäärä770

KustantajaSpringer

JulkaisumaaSingapore

Julkaisun kielienglanti

DOIhttps://doi.org/10.1007/978-981-97-0855-0_62

Julkaisun avoin saatavuusEi avoin

Julkaisukanavan avoin saatavuus


Tiivistelmä

Survival analysis is of paramount importance in guiding the development of optimal treatment strategies for cancer patients. Because of the rich prognostic information contained in whole slide images (WSIs), multiple instance learning (MIL) approaches integrated with WSI analysis have been widely used in survival risk prediction. However, existing MIL methods often fail to encompass the complete range of histological image features, including critical information from local tissue regions, which limits their performance in survival prognosis. To address this limitation, we employ a self-supervised learning mechanism to train a feature extractor which can capture the intricate characteristics of WSIs. Furthermore, we introduce an attention mechanism that incorporates local patches and clusters to guide the fusion of multi-level features for survival outcome prediction. The proposed method demonstrates excellent performance on the widely recognized TCGA-COAD dataset. Experimental findings indicate that the integration of pre-trained feature extractors with MIL method and the fusion of multi-level histological features yield notable advancements in survival risk predictions.


YSO-asiasanatsyöpätauditennusteethistologiakonenäkökoneoppiminen


Liittyvät organisaatiot


OKM-raportointiKyllä

Raportointivuosi2024

Alustava JUFO-taso1


Viimeisin päivitys 2024-15-06 klo 21:06