A robust and lightweight deep attention multiple instance learning algorithm for predicting genetic alterations. (April 2023)

• Record Type: Journal Article
• Authors: Guo, Bangwei; Li, Xingyu; Yang, Miaomiao; Zhang, Hong; Xu, Xu Steven
• Is Part Of: Computerized medical imaging and graphics. Volume 105(2023)
• Publication Date: 2023-04
• Subjects: Deep learning -- Whole slide images -- Gene Mutation -- Mutiple Instance Learning -- Attention mechanism; Diagnostic imaging -- Periodicals; Imaging systems in medicine -- Periodicals; Diagnosis, Radioscopic -- Data processing -- Periodicals; Diagnostic Imaging -- Periodicals; Imagerie pour le diagnostic -- Périodiques; Diagnostic imaging; Periodicals; Electronic journals; Electronic journals; 616.0754
• DOI: 10.1016/j.compmedimag.2023.102189
• Languages: English
• ISSNs: 0895-6111

Abstract

Abstract: Self-attention mechanism-based algorithms are attractive in digital pathology due to their interpretability, but suffer from computation complexity. This paper presents a novel, lightweight Attention-based Multiple Instance Mutation Learning (AMIML) model to allow small-scale attention operations for predicting gene mutations. Compared to the standard self-attention model, AMIML reduces the number of model parameters by approximately 70%. Using data for 24 clinically relevant genes from four cancer cohorts in TCGA studies (UCEC, BRCA, GBM, and KIRC), we compare AMIML with a standard self-attention model, five other deep learning models, and four traditional machine learning models. The results show that AMIML has excellent robustness and outperforms all the baseline algorithms in the vast majority of the tested genes. Conversely, the performance of the reference deep learning and machine learning models vary across different genes, and produce suboptimal prediction for certain genes. Furthermore, with the flexible and interpretable attention-based pooling mechanism, AMIML can further zero in and detect predictive image patches. Highlights: The lightweight AMIML model allows small-scale self-attention and quicker turnaround compared to standard self-attention models. AMIML shows excellent robustness and outperforms other existing deep learning and machine learning models. AMIML improves the prediction of a wide range of genes over the current literature reports. The interpretability of AMIML workflow can help pathologists to zero in on important ROIs