Abstract
Breast cancer is a significant global health concern, particularly for women. Early detection and appropriate treatment are crucial in mitigating its impact, with histopathology examinations playing a vital role in swift diagnosis. However, these examinations often require a substantial workforce and experienced medical experts for proper recognition and cancer grading. Automated image retrieval systems have the potential to assist pathologists in identifying cancerous tissues, thereby accelerating the diagnostic process. Nevertheless, due to considerable variability among the tissue and cell patterns in histological images, proposing an accurate image retrieval model is very challenging. This work introduces a novel attention-based adversarially regularized variational graph autoencoder model for breast histological image retrieval. Additionally, we incorporated cluster-guided contrastive learning as the graph feature extractor to boost the retrieval performance. We evaluated the proposed model's performance on two publicly available datasets of breast cancer histological images and achieved superior or very competitive retrieval performance, with average mAP scores of 96.5% for the BreakHis dataset and 94.7% for the BACH dataset, and mVP scores of 91.9% and 91.3%, respectively. Our proposed retrieval model has the potential to be used in clinical settings to enhance diagnostic performance and ultimately benefit patients.
Abstract (translated)
乳腺癌是一个全球健康问题，特别是对女性的影响非常大。早期诊断和适当的治疗对减轻其影响至关重要，而组织病理学检查在迅速诊断中发挥着关键作用。然而，这些检查通常需要大量的工作力和经验丰富的医疗专家来进行适当的识别和癌症分级。自动图像检索系统有可能帮助病理学家识别出恶性组织，从而加速诊断过程。然而，由于组织和细胞在组织病理图中的变异很大，提出准确的组织病理图检索模型非常具有挑战性。本工作提出了一种新颖的关注基于对抗训练的变分图自编码器模型用于乳腺癌组织病理图检索。此外，我们还引入了聚类引导的对比学习作为图特征提取器，以提高检索性能。我们在两个公开可用的乳腺癌组织病理图数据集上评估所提出的模型的性能，实现了卓越或非常竞争力的检索性能，平均mAP得分分别为96.5%的BreakHis数据集和94.7%的BACH数据集，平均mVP得分分别为91.9%和91.3%。我们提出的检索模型有望在临床实践中提高诊断性能，最终为患者带来好处。
URL
https://arxiv.org/abs/2405.04211
