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相关概念视频

Computed Tomography01:10

Computed Tomography

Tomography refers to imaging by sections. Computed tomography (CT) is a non-invasive imaging technique that uses computers to analyze several cross-sectional X-rays to reveal minute details about structures in the body.
The technique was invented in the 1970s and is based on the principle that as X-rays pass through the body, they are absorbed or reflected at different levels. In the technique, a patient lies on a motorized platform while a computerized axial tomography (CAT) scanner rotates...
Imaging Studies I: CT and MRI01:14

Imaging Studies I: CT and MRI

Introduction: MRI and CT scans are crucial advancements in medical imaging techniques, playing a vital role in diagnosing conditions related to the gastrointestinal (GI) system. Each scan serves distinct purposes, targets specific areas, and requires unique nursing duties.
Description of the Procedures
Computed Tomography (CT) scan:
Computed Tomography (CT) scans use X-ray technology to generate detailed images of bones, organs, and tissues. During the scan, the patient lies on a moving table...
Imaging Studies for Cardiovascular System IV: CMRI01:21

Imaging Studies for Cardiovascular System IV: CMRI

Cardiovascular magnetic resonance imaging, or CMRI, is a non-invasive diagnostic test that employs a magnetic field and radiofrequency waves to create precise images of the heart and arteries. It provides comprehensive information about cardiac anatomy, function, perfusion, and tissue characterization without ionizing radiation.IndicationsCMRI diagnoses various heart conditions, including tissue damage from heart attacks, ischemic heart disease, myocarditis, aortic issues (tears, aneurysms,...
Imaging Studies for Cardiovascular System V: CT01:28

Imaging Studies for Cardiovascular System V: CT

Cardiac computed tomography (CT) scanning is an advanced cardiac imaging technique that utilizes CT technology, with or without intravenous (IV) contrast, to produce accurate cross-sectional virtual slices of specific areas of the heart, coronary circulation, and major blood vessels such as the aorta, pulmonary veins, and arteries. The computer processes these slices to generate three-dimensional images. Multidetector CT (MDCT) is a rapid form of CT scanning that captures multiple slices...
Imaging Studies III: Computed Tomography01:27

Imaging Studies III: Computed Tomography

DefinitionComputed Tomography (CT) of the genitourinary (GU) tract is a non-invasive imaging modality that utilizes X-rays and computer processing to generate detailed cross-sectional images of the urinary system, encompassing the kidneys, ureters, bladder, and adjacent structures such as the adrenal glands.PurposeCT scans of the GU tract serve several diagnostic and therapeutic purposes, including:Diagnosis of Urinary Tract Diseases: Detects kidney stones, tumors, cysts, and congenital...
Imaging Studies IV: Magnetic Resonance Imaging01:27

Imaging Studies IV: Magnetic Resonance Imaging

Introduction:Magnetic Resonance Imaging, or MRI, can include a specialized imaging technique of the urinary system known as Magnetic Resonance Urography (MRU). This radiation-free technique uses strong magnetic fields and radio waves to produce detailed images with the help of a computer. MRU is particularly effective for visualizing fluid-filled structures like the kidneys, ureters, and bladder.Applications of MRI in the Genitourinary SystemKidneys and Ureters: MRI detects tumors, cysts,...

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相关实验视频

Updated: Jun 19, 2026

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images
14:08

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Published on: April 13, 2013

CoMIL:一个对比的CNN-变压器框架,用于全幻灯片病理图像分类的多实例学习.

Bowen Liu, Hongbo Zhu, Xiaotong Wei

    IEEE journal of biomedical and health informatics
    |March 3, 2026
    PubMed
    概括

    CoMIL是一个双分支框架,通过平衡全球上下文和地方细节来增强整个幻灯片图像 (WSI) 的分类. 这种方法提高了数字病理学任务的准确性,优于现有的方法.

    科学领域:

    • 计算病理学计算病理学
    • 数字病理学数字病理学
    • 机器学习用于医学成像.

    背景情况:

    • 整个幻灯片图像 (WSI) 分类由于千兆像素尺度和监督薄弱而带来了挑战.
    • 现有的方法在WSI分析中难以平衡全球背景与地方细节.
    • 数据集中的弱标签可能导致模型稳定性降低.

    研究的目的:

    • 提出 CoMIL,一个新的双分支框架,以改进 WSI 分类.
    • 解决单流网络在捕获全球和本地信息方面的局限性.
    • 为了提高空间意识和模型的稳定性对标签噪声在WSI数据.

    主要方法:

    • CoMIL使用双分支架构,其中一个用于远程依赖的变压器分支和一个用于本地形态的CNN分支.
    • 引入了一个超定位生成器 (HyperPG) 模块,以使用多尺度适应机制和可变形卷曲来减轻空间信息丢失.
    • 与KL分歧最小化的对称互学习用于提高对弱标签噪声的稳定性.

    主要成果:

    • 在Camelyon16数据集上,CoMIL实现了98.6%和95.3%的曲线下面面积 (AUC) 准确度.
    • 该方法在TCGA_脏数据集上达到98.8%和93.3%的AUC准确度.

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    A Live-cell Image-Based Machine Learning Strategy to Monitor Pluripotent Stem Cell Differentiation

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    Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images
    14:08

    Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

    Published on: April 13, 2013

    A Live-cell Image-Based Machine Learning Strategy to Monitor Pluripotent Stem Cell Differentiation
    11:38

    A Live-cell Image-Based Machine Learning Strategy to Monitor Pluripotent Stem Cell Differentiation

    Published on: October 4, 2024

  • 在这两组数据中,性能超过了已知的高级WSI分类方法.
  • 结论:

    • 拟议的CoMIL框架有效地平衡了WSI分类的全球背景和当地细节.
    • 超级PG模块通过线性复杂度增强了空间意识,改进了WSI分析.
    • 对称的相互学习增强了模型的稳定性,使其适用于弱监督的WSI分类任务.