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Imaging Studies I: Kidney, Ureter, and Bladder Studies

Imaging Studies I: Kidney, Ureter, and Bladder Studies

Kidney, Ureter, and Bladder (KUB) StudiesKidney, Ureter, and Bladder (KUB) studies are standard diagnostic imaging procedures used to assess the anatomy of the urinary system. They are commonly utilized for patients experiencing abdominal pain or urinary symptoms. By using a simple X-ray of the abdomen, KUB studies can reveal structural and pathological abnormalities within the kidneys, ureters, and bladder. These studies are particularly valuable in diagnosing kidney stones, urinary...

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Updated: Jan 12, 2026

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

基于变压器的多类细分管道用于基本的组织学.

Junling He^1,2, Pieter A Valkema^1,3, Jingmin Long⁴

¹Department of Pathology, LUMC, Leiden, The Netherlands.

Scientific reports

|November 6, 2025

概括

此摘要是机器生成的。

基于变压器的模型,如M2F-Swin-B,在细分组织学方面表现出卓越的性能,特别是在受损区域,与基于CNN的模型相比,如UNet-ResNet18.

关键词:

深度学习是一种深度学习.脏组织学脏组织学多类细分化的多类细分化.语义细分语义细分是指语义细分.变压器变压器变压器

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Last Updated: Jan 12, 2026

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

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Published on: July 5, 2024

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科学领域:

脏病理学脏病理学
计算病理学计算病理学
医疗图像分析医学图像分析

背景情况:

病理学的深度学习主要针对形态学,对严重损伤的模型多功能性进行的研究有限.
在不同数据分布 (模式/域位移) 上评估模型性能对于临床适用性至关重要.

研究的目的:

在病理学中比较基于卷积神经网络 (CNN) 和基于变压器的深度学习模型的模式/域移位能力.
评估模型在细分组织学中的性能,特别是在严重损伤,纤维化和炎症的区域.

主要方法:

为了模拟多中心数据分布,使用了两个分割策略 (WSI级和补丁级).
在这些策略上训练和比较了基于CNN (UNet-ResNet18) 和基于变压器 (M2F-Swin-B) 的模型.
模型在外部数据集上得到验证,并对纤维化和炎症水平进行敏感性分析.

主要成果:

M2F-Swin-B在整个联盟 (A-IoU) 的平均交叉和每个类的IoU中显著超过了UNet-ResNet18,无论是在补丁级和WSI级.
M2F-Swin-B在纤维化和炎症程度较高的区域表现出卓越的表现,并实现了较高的动脉IOU.
在Mask2Former (用于M2F-Swin-B) 中的注意力机制导致了更清晰,更均的细分,特别是在有限的数据下.

结论:

基于变压器的模型,特别是M2F-Swin-B,与基于CNN的模型相比,为脏组织学细分提供了更好的多功能性和性能.
开发的多类细分管道对于脏组织学分析是强大的.
变压器模型中的注意力机制有利于细分质量,即使在数据稀缺的场景中也是如此.