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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

从基因病理图像进行核实例细分,使用基于贝叶斯脱落的深度学习.

Naga Raju Gudhe¹, Veli-Matti Kosma^2,3, Hamid Behravan²

¹Institute of Clinical Medicine, Pathology and Forensic Medicine, Multidisciplinary Cancer research community RC Cancer, University of Eastern Finland, P.O. Box 1627, Kuopio, 70211, Finland. raju.gudhe@uef.fi.

BMC medical imaging

|October 19, 2023

概括

这项研究引入了贝叶斯的深度学习模型,用于细胞核细分在组织病理学图像中,通过量化预测不确定性来优于现有的方法,从而在医学图像分析中获得更可靠的结果.

关键词:

贝叶斯深度学习是贝叶斯的深度学习.数字病理学数字病理学医疗图像分析医学图像分析核心细分的核心细分.语义细分语义细分是指语义细分.不确定性估计估计不确定性

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Last Updated: Jul 12, 2025

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科学领域:

医学图像分析医学图像分析
计算病理学计算病理学
深度学习 (Deep Learning) 是一种深度学习.

背景情况:

确定性深度学习模型在医学图像分析中表现出色,包括核细分,但缺乏对预测的信心评估.
目前的模型将准确性优先于对关键诊断任务的预测可靠性的量化.

研究的目的:

用贝叶斯表示法来开发一个语义细分模型,用于在基因病理学图像中的核细分.
在模型预测中量化认识不确定性,以提高可靠性.
通过不确定性估计,提高医学图像分析的诊断准确性.

主要方法:

提出了贝叶斯的深度学习模型,用于核的语义细分.
在预测不确定性估计的推断过程中,雇员蒙特卡洛 (MC) 失业.
在PanNuke数据集上评估性能,与U-Net,SegNet和Hover-net.net进行比较.

主要成果:

在PanNuke数据集上获得了0.893 ± 0.008的平均F1分数和0.868 ± 0.003的IOU.
性能优于最先进的Hover-net (F1: 0.871 ± 0.010,IU: 0.840 ± 0.032) 的性能.
证明了优越的核细分精度和可靠的不确定性量化.

结论:

贝叶斯的深度学习方法与MC脱落提供了优越的核细分性能在他的病理学.
纳入认识系统不确定性估计导致医疗图像分析的更可靠的预测.
这项工作有助于开发更准确,更可靠的计算机辅助诊断系统.