经典骨髓增殖性肿瘤分类深度学习方法中的超参数调优

U. K. M. Yusof, S. Mashohor, M. Hanafi, S. Noor, Norsafina, Zainal
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引用次数: 0

摘要

组织病理学图像是定义生物组成或检查细胞和组织组成的重要资源。组织病理学图像的分析对于支持不同类型的疾病也至关重要,包括骨髓增生性肿瘤(MPN)等罕见疾病。尽管诊断工具的技术进步促进了MPN的分类过程,但骨髓穿刺术(BMT)获得的组织病理学图像的形态学评估仍然是确认MPN亚型的关键。然而,目前的评估结果是非常具有挑战性的,因为主观的,重复性差的标准和高度依赖于病理学家,这导致了观察者之间的解释差异。为了解决这一问题,本研究采用深度学习方法对经典MPN进行了分类,即真性红细胞增多症(PV)、原发性血小板增多症(ET)和原发性骨髓纤维化(MF)。数据收集经历了几个图像增强过程,以增加特征可变性并扩展数据集。然后将增强后的图像输入CNN分类器,并实现交叉验证方法。最后,采用Adamax优化器对最佳分类模型进行了优化,准确率达到95.3%。该模型给出的高精度和最佳输出显示了MPN分类部署的巨大潜力,因此有助于超越传统方法的样本解释和监测。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Hyperparameter Tuning in Deep Learning Approach for Classification of Classical Myeloproliferative Neoplasm
Histopathology images are an essential resource for defining biological compositions or examining the composition of cells and tissues. The analysis of histopathology images is also crucial in supporting different class of disease including for rare disease like Myeloproliferative Neoplasms (MPN). Despite technological advancement in diagnostic tools to boost procedure in classification of MPN, morphological assessment from histopathology images acquired by bone marrow trephine (BMT) is remained critical to confirm MPN subtypes. However, the outcome of assessment at a present is profoundly challenging due to subjective, poorly reproducible criteria and highly dependent on pathologist where it caused interobserver variability in the interpretation. To address, this study developed a classification of classical MPN namely polycythemia vera (PV), essential thrombocythemia (ET) and primary myelofibrosis (MF) using deep learning approach. Data collection was undergoing several image augmentations processes to increase features variability and expand the dataset. The augmented images were then fed into CNN classifier followed by implementation of cross validation method. Finally, the best classification model was performed 95.3% of accuracy by using Adamax optimizer. High accuracy and best output given by proposed model shows significant potential in the deployment of the classification of MPN and hence facilitates the interpretation and monitoring of samples beyond conventional approaches.
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