A novel convolution transformer-based network for histopathology-image classification using adaptive convolution and dynamic attention

Mahmood, Tahir; Wahid, Abdul; Hong, Jin Seong; Kim, Seung Gu; Park, Kang Ryoung

doi:10.1016/j.engappai.2024.108824

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Cited by 5 publications

References 43 publications

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An efficient enhanced feature framework for grading of renal cell carcinoma using Histopathological Images

Maqsood,

Wang,

Ali

et al. 2024

Appl Intell

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An efficient enhanced feature framework for grading of renal cell carcinoma using Histopathological Images

Maqsood,

Wang,

Ali

et al. 2024

Appl Intell

View full text Add to dashboard Cite

Category-weight instance fusion learning for unsupervised domain adaptation on breast cancer histopathology images

Zhang,

Chen,

Lei

2025

Biomedical Signal Processing and Control

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LIMPACAT : Multi-Omics Attention Transformer for Immune Prediction in Liver Cancer Using Whole-Slide Imaging

Chiu

2024

Preprint

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This study introduces LIMPACAT (Liver Immune Microenvironment Prediction and Classification Attention Transformer), a framework that leverages whole-slide images (WSIs) to predict immune cell levels associated with prognosis in liver cancer. Since direct immune cell composition data is unavailable in the TCGA-LIHC dataset, we inferred immune cell levels using liver-specific single-cell RNA sequencing (scRNA-seq) data to simulate bulk RNA-seq profiles, allowing estimation of immune compositions within the tumor microenvironment. To ensure consistency, we tested three normalization methods-log normalization, canonical correlation analysis (CCA), and SCTransform-for scRNA-seq data integration and preprocessing, which facilitated reliable predictions of immune cell distributions in bulk RNA-seq. By applying this scRNA-seq-informed cell composition deconvolution model to real bulk RNA-seq data from liver cancer samples, we confirmed alignment in immune cell composition estimates between bulk RNA-seq and scRNA-seq. Using a multiple instance learning (MIL) framework with an attention transformer, LIMPACAT achieved approximately 80% accuracy in classifying immune cell levels relevant to patient prognosis. These findings highlight the feasibility of integrating WSIs and multi-omics data to enhance immune profiling and prognostic predictions in liver cancer research.

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A novel convolution transformer-based network for histopathology-image classification using adaptive convolution and dynamic attention

Cited by 5 publications

References 43 publications

An efficient enhanced feature framework for grading of renal cell carcinoma using Histopathological Images

An efficient enhanced feature framework for grading of renal cell carcinoma using Histopathological Images

Category-weight instance fusion learning for unsupervised domain adaptation on breast cancer histopathology images

LIMPACAT : Multi-Omics Attention Transformer for Immune Prediction in Liver Cancer Using Whole-Slide Imaging

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