组织病变性癌症图像分类与登革
Elva Estrada-Estrada1, Aldo Ramirez-Arellano2, Pilar Ortiz-Vilchis1
1Seccion de Estudios de Posgrado e Investigacion, Escuela Superior de Medicina Instituto Politecnico Nacional, Ciudad de Mexico, Mexico.
Biomedical physics & engineering express
|September 17, 2025
概括
这项研究介绍了登革和双向长期短期记忆 (bLSTM) 网络,用于在组织病理图像中准确地分类癌症. 这种新的方法有效地将正常与异常组织区分开来,在多个数据集中实现高准确率.
科学领域:
- 数字病理学数字病理学
- 计算生物学是一种计算生物学.
- 医学成像分析分析 医学成像分析
背景情况:
- 组织病理学成像对于瘤检测,诊断和分类至关重要.
- 像反复神经网络 (RNN) 和卷积神经网络 (CNN) 这样的深度学习模型已经改善了数字病理学.
- 使用Tsallis和Shannon输入的现有方法面临着噪音和不确定性的挑战.
研究的目的:
- 使用登革和双向长期短期记忆 (bLSTM) 网络对组织病理癌症图像进行分类.
- 为病理学家开发一种新的方法,用于准确区分正常和异常组织.
- 评估氏在捕捉复杂性和改善癌症分类方面的有效性.
主要方法:
- 在各种尺度 (盒子尺寸) 上使用盒子覆盖方法计算的登格.
- 利用的作为双向LSTM (bLSTM) 网络的输入向量来获得的信息维度.
- 分析了三个组织病理学数据集:BreakHis (乳腺),肺结肠和PANDA (前列腺).
主要成果:
- 在二进制乳腺癌分类 (0.98) 中实现了高精度.
- 达到0.99.9的多类分类准确度.
- 在肺癌 (0.98) 和结肠癌 (0.99) 图像分类中表现出色.
- 在前列腺癌图像分类中获得0.924准确度.
结论:
- 登革与bLSTM网络相结合,为乳腺,结肠和肺癌的组织病理图像提供了精确的分类系统.
- 提出的方法有效地减轻了噪音和不确定性,从而使得癌症的分类得到了满意.
- 这种创新方法提高了病理学家区分正常和异常组织的能力.
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