论文标题
使用视觉变压器的深锚注意力学习对治疗方法的脑癌生存预测
Brain Cancer Survival Prediction on Treatment-na ive MRI using Deep Anchor Attention Learning with Vision Transformer
论文作者
论文摘要
基于图像的脑癌预测模型，基于放射线学，从磁共振成像（MRI）中量化了放射性表型。但是，由于采集和预处理管道的可变性，这些特征很难复制。尽管有肿瘤内表型异质性的证据，但使用这种方法，MRI扫描中不同切片之间的不同切片之间的空间多样性是相对未探索的。在这项工作中，我们提出了一种深刻的锚点注意聚集策略，并具有视觉变压器，以预测脑癌患者的生存风险。提出了深入的锚点注意学习（DAAL）算法，以将不同的权重分配给具有可训练距离测量值的切片级别的表示。我们评估了n = 326 MRI的方法。我们的结果表现优于多个基于学习的技术。 Daal强调了关键切片的重要性，并证实了临床直觉，即切片间空间多样性可以反映疾病的严重程度，并与结果有关。
Image-based brain cancer prediction models, based on radiomics, quantify the radiologic phenotype from magnetic resonance imaging (MRI). However, these features are difficult to reproduce because of variability in acquisition and preprocessing pipelines. Despite evidence of intra-tumor phenotypic heterogeneity, the spatial diversity between different slices within an MRI scan has been relatively unexplored using such methods. In this work, we propose a deep anchor attention aggregation strategy with a Vision Transformer to predict survival risk for brain cancer patients. A Deep Anchor Attention Learning (DAAL) algorithm is proposed to assign different weights to slice-level representations with trainable distance measurements. We evaluated our method on N = 326 MRIs. Our results outperformed attention multiple instance learning-based techniques. DAAL highlights the importance of critical slices and corroborates the clinical intuition that inter-slice spatial diversity can reflect disease severity and is implicated in outcome.
