Correction: A pathology-attention multi-instance learning framework for multimodal classification of colorectal lesions

Affiliations

¹ Department of Life and Health, Shenzhen International Graduate School, Tsinghua University, Shenzhen, Guangdong, China.
² Department of Pathology, Liuzhou People's Hospital Affiliated to Guangxi Medical University, Liuzhou, Guangxi, China.
³ Department of Statistics and Data Science, Washington University in St. Louis, St. Louis, MO, United States.
⁴ State Key Laboratory of Cancer Biology, Department of Pathology, Xijing Hospital and School of Basic Medicine, Fourth Military Medical University, Xi'an, China.

^# Contributed equally.

Published Erratum

Fanglei Fu et al. Front Pharmacol. 2025.

. 2025 Jul 24:16:1666330.

doi: 10.3389/fphar.2025.1666330. eCollection 2025.

¹ Department of Life and Health, Shenzhen International Graduate School, Tsinghua University, Shenzhen, Guangdong, China.
² Department of Pathology, Liuzhou People's Hospital Affiliated to Guangxi Medical University, Liuzhou, Guangxi, China.
³ Department of Statistics and Data Science, Washington University in St. Louis, St. Louis, MO, United States.
⁴ State Key Laboratory of Cancer Biology, Department of Pathology, Xijing Hospital and School of Basic Medicine, Fourth Military Medical University, Xi'an, China.

^# Contributed equally.

Abstract

[This corrects the article DOI: 10.3389/fphar.2025.1592950.].

Keywords: colorectal cancer; multimodal learning; pathology attention; weakly supervised learning; whole slide image classification.

A pathology-attention multi-instance learning framework for multimodal classification of colorectal lesions.
Fu F, Zhang X, Wang Z, Xie L, Fu M, Peng J, Wu J, Wang Z, Guan T, He Y, Lin JS, Zhu L, Dai W. Fu F, et al. Front Pharmacol. 2025 Jun 6;16:1592950. doi: 10.3389/fphar.2025.1592950. eCollection 2025. Front Pharmacol. 2025. PMID: 40548052 Free PMC article.