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Machines are complex structures consisting of movable, pin-connected multi-force members that work together to transmit forces. One example of a machine is the cutting plier, which is used to cut wires by applying forces to its handles. When equal and opposite forces are exerted on the handles of the cutting plier, they cause the cutting edges to come together and apply equal and opposite reaction forces on the wire, which are greater than the applied forces.
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A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

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Published on: January 11, 2020

Machine learning approaches for pathologic diagnosis.

Daisuke Komura¹, Shumpei Ishikawa²

¹Department of Preventive Medicine, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-0033, Japan.

Virchows Archiv : an International Journal of Pathology

|June 22, 2019

Summary

This summary is machine-generated.

Deep learning excels in medical image analysis, outperforming pathologists in some cases. This review explores machine learning applications and challenges in histopathological image analysis for improved diagnosis.

Keywords:

Deep learning Digital pathology Machine learning WSI (whole slide image)

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Area of Science:

Computer Science
Medicine
Pathology

Background:

Deep learning, particularly convolutional neural networks, has shown remarkable success in general image recognition.
These techniques are increasingly applied to medical imaging, including histopathology, to aid diagnosis.
Deep learning algorithms have demonstrated performance exceeding that of experienced pathologists in specific histopathological image recognition tasks.

Purpose of the Study:

To review current machine learning applications in assisting pathologic diagnosis.
To identify and describe the unique challenges of machine learning for histopathological image analysis.
To explore potential solutions for overcoming these challenges in the field.

Main Methods:

Review of existing literature on machine learning applications in pathology.
Analysis of the specific characteristics of histopathological images that necessitate specialized machine learning approaches.
Discussion of current and proposed methods for addressing the interpretability and accuracy of deep learning models in this domain.

Main Results:

Machine learning, especially deep learning, offers significant potential to enhance diagnostic accuracy and efficiency in pathology.
Histopathological image analysis presents unique challenges compared to general image recognition, requiring tailored machine learning strategies.
The interpretability of deep learning models remains a critical concern for pathologists, impacting clinical adoption.

Conclusions:

Machine learning holds great promise for revolutionizing pathologic diagnosis through advanced image analysis.
Addressing the specific complexities of histopathological data and improving model transparency are key to successful implementation.
Further research into specialized learning methods and interpretable artificial intelligence is crucial for bridging the gap between AI capabilities and clinical practice.