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Related Concept Videos

Imaging Biological Samples with Optical Microscopy01:18

Imaging Biological Samples with Optical Microscopy

Optical microscopy uses optic principles to provide detailed images of samples. Antonie van Leeuwenhoek designed the first compound optical microscope in the 17th century to visualize blood cells, bacteria, and yeast cells. In 1830, Joseph Jackson Lister created an essentially modern light microscope. The 20th century saw the development of microscopes with enhanced magnification and resolution.
In optical microscopy, the specimen to be viewed is placed on a glass slide and clipped on the stage...
Confocal Fluorescence Microscopy01:16

Confocal Fluorescence Microscopy

Confocal microscopy is an advanced microscopic technique. The prime advantage of the confocal microscope over other microscopy techniques is its ability to block the out-of-focus light from the illuminated samples using pinholes. It is widely used with fluorescence optics to obtain high-resolution, sharp contrast images. Unlike optical microscopes, confocal microscopes use a focused beam of light laser to scan the entire sample surface at different z-planes. These microscopes are, therefore,...
Three-Dimensional Microscopy in Microbiology01:28

Three-Dimensional Microscopy in Microbiology

Three-dimensional imaging techniques are essential in cell biology, allowing researchers to visualize intricate cellular structures with high resolution. Two prominent methods, Differential Interference Contrast Microscopy (DIC) and Confocal Scanning Laser Microscopy (CSLM), provide distinct advantages for imaging live and thick specimens, respectively.Differential Interference Contrast MicroscopyDIC microscopy enhances contrast in transparent, unstained samples by converting phase...

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Related Experiment Video

Updated: Jun 18, 2026

Automated Slide Scanning and Segmentation in Fluorescently-labeled Tissues Using a Widefield High-content Analysis System
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Exploring multi-instance learning in whole slide imaging: Current and future perspectives.

Jikai Yu1, Hongda Chen1, Lianxin Hu1

  • 1School of Information Engineering, Huzhou University, Huzhou, ZheJiang 313000, China.

Pathology, Research and Practice
|May 14, 2025
PubMed
Summary
This summary is machine-generated.

Whole slide images (WSIs) are crucial for pathology but too large for GPUs. Multi-instance learning (MIL) effectively analyzes these large WSIs for automated diagnostics.

Keywords:
Deep learningMIL applicationsMulti-instance learning(MIL)Whole slide image

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Area of Science:

  • Digital Pathology
  • Computational Pathology
  • Machine Learning in Medicine

Background:

  • Whole slide images (WSIs) are essential for diagnostic pathology but pose computational challenges due to their large size.
  • Directly processing gigabyte-scale WSIs on GPUs is difficult, hindering automated screening and diagnostic algorithm development.

Purpose of the Study:

  • To systematically review research progress and applications of multi-instance learning (MIL) in analyzing WSIs.
  • To outline MIL's advantages and improvements for WSI handling and analyze mainstream techniques for cancer detection and subtype classification.

Main Methods:

  • Systematic literature review of over 90 articles from Web of Science, IEEE Xplore, and PubMed.
  • Analysis of core characteristics and performance of mainstream MIL techniques applied to WSI analysis.
  • Focus on methods enhancing MIL capabilities for WSI processing.

Main Results:

  • Multi-instance learning (MIL) offers an effective computational framework for WSI analysis.
  • Techniques such as data preprocessing, multi-scale feature fusion, instance selection, and Transformer models significantly improve MIL performance on WSIs.
  • MIL shows promise in tasks like cancer detection and subtype classification from WSIs.

Conclusions:

  • MIL is a powerful approach for overcoming computational limitations in WSI analysis.
  • Advancements in MIL methods are crucial for developing automated pathological diagnostic tools.
  • Future research should focus on further enhancing MIL technologies for broader adoption in pathology.