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Nuclear Localization Signals and Import

Nuclear Localization Signals and Import

Proteins targeted to the nucleus carry short stretches of amino acid sequences called the nuclear localization signal or NLS. Classical nuclear localization signals are of two types: monopartite and bipartite NLS. Monopartite classical NLS (cNLS) consists of a single cluster of 4-8 amino acids. Bipartite cNLS consists of two clusters of 2-3 amino acids and a 9-12 residue long proline-rich linker bridging the two clusters. Signal clusters are rich in positively charged amino acids such as...

Aggregates Classification

Aggregates Classification

Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...

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相关实验视频

Updated: Jul 26, 2025

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Published on: November 11, 2022

微笑:对成本敏感的多任务学习,用于核细分和分类与不平衡的注释.

Xipeng Pan¹, Jijun Cheng², Feihu Hou³

¹School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin, Guangxi 541004, China; Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, Guangdong 510080, China; Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, Guangdong 510080, China.

Medical image analysis

|June 22, 2023

概括

此摘要是机器生成的。

本研究引入了一种新的框架 (SMILE),通过解决数据异质性来改进整个幻灯片图像的核细分和分类. 该方法提高了生物和临床应用的特征表示和细分精度.

关键词:

具有成本敏感性的成本.没有平衡的注释.多任务相关性注意力注意力核细分和核分类和核分类.

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相关实验视频

Last Updated: Jul 26, 2025

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Published on: November 11, 2022

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

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

科学领域:

计算病理学计算病理学
数字病理学数字病理学
生物医学图像分析

背景情况:

精确的核细分和整片图像 (WSIs) 中的分类对于生物分析,临床诊断和精密医学至关重要.
目前的方法与核异质性,特别是不平衡的数据分布和多样化的形态扎,导致占主导地位的少数阶级和脆弱的细分.

研究的目的:

开发一个强大的框架,以解决WSIs核细分和分类中的数据异质性.
提高复杂生物样本中核分析的准确性和可靠性.

主要方法:

提出了一个成本敏感的多任务学习 (SMILE) 框架.
引入了多任务关联注意力 (MTCA) 机制,通过交互相关任务来增强特征表示.
实施了成本敏感的学习策略,以惩罚少数阶级错误分类.
开发了一种粗细标记控制的分水后处理步骤,以改善模糊轮的大核的细分.

主要成果:

在CoNSeP和MoNuSAC 2020数据集上,SMILE框架实现了最先进的性能.
在处理不平衡数据和多样化核形态方面表现出显著的改进.
拟议的MTCA和后处理步骤有效地提高了细分和分类的准确性.

结论:

在WSI分析中,SMILE框架为核异质性挑战提供了强有力的解决方案.
这种方法提升了计算病理学的能力,用于更准确的诊断和精准医学.
开发的方法为未来在自动化基因病理图像分析方面的研究提供了基础.