Automated mitosis detection in stained histopathological images using Faster R-CNN and stain techniques.

IF 1.8 Q3 MATHEMATICAL & COMPUTATIONAL BIOLOGY
Jesús García-Salmerón, José Manuel García, Gregorio Bernabé, Pilar González-Férez
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引用次数: 0

Abstract

Accurate mitosis detection is essential for cancer diagnosis and treatment. Traditional manual counting by pathologists is time-consuming and may cause errors. This research investigates automated mitosis detection in stained histopathological images using Deep Learning (DL) techniques, particularly object detection models. We propose a two-stage object detection model based on Faster R-CNN to effectively detect mitosis within histopathological images. The stain augmentation and normalization techniques are also applied to address the significant challenge of domain shift in histopathological image analysis. The experiments are conducted using the MIDOG++ dataset, the most recent dataset from the MIDOG challenge. This research builds on our previous work, in which two one-stage frameworks, in particular on RetinaNet using fastai and PyTorch, are proposed. Our results indicate favorable F1-scores across various scenarios and tumor types, demonstrating the effectiveness of the object detection models. In addition, Faster R-CNN with stain techniques provides the most accurate and reliable mitosis detection, while RetinaNet models exhibit faster performance. Our results highlight the importance of handling domain shifts and the number of mitotic figures for robust diagnostic tools.

使用Faster R-CNN和染色技术在染色的组织病理学图像中自动检测有丝分裂。
准确的有丝分裂检测对癌症的诊断和治疗至关重要。病理学家传统的手工计数既耗时又可能导致错误。本研究利用深度学习(DL)技术,特别是对象检测模型,研究染色组织病理学图像中有丝分裂的自动检测。我们提出了一种基于Faster R-CNN的两阶段目标检测模型,以有效检测组织病理图像中的有丝分裂。染色增强和归一化技术也被应用于解决组织病理图像分析领域转移的重大挑战。实验使用MIDOG++数据集进行,这是MIDOG挑战的最新数据集。本研究建立在我们之前的工作基础上,其中提出了两个单阶段框架,特别是使用fastai和PyTorch的RetinaNet框架。我们的研究结果表明,在各种场景和肿瘤类型中都有良好的f1得分,证明了目标检测模型的有效性。此外,更快的R-CNN与染色技术提供了最准确和可靠的有丝分裂检测,而RetinaNet模型表现出更快的性能。我们的研究结果强调了处理区域转移和有丝分裂图的数量对于稳健诊断工具的重要性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Journal of Integrative Bioinformatics
Journal of Integrative Bioinformatics Medicine-Medicine (all)
CiteScore
3.10
自引率
5.30%
发文量
27
审稿时长
12 weeks
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