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Related Concept Videos

Classifying Matter by Composition03:35

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Matter: Pure Substances and Mixtures
According to its composition, the matter can be classified into two broad categories — pure substances and mixtures. 
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Chemistry is the study of matter and the changes it undergoes. Matter is anything that has mass and occupies space. Matter is all around us; the air, water, soil, mountains, even our bodies are all examples of matter. Matter is divided into three states — solid, liquid, and gas — that are commonly found on earth. The fourth state of matter, plasma, occurs naturally in the interiors of stars. 
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Cancers arise due to mutations in genes involved in the regulation of cell division, which leads to unrestricted cell proliferation. Modern science and medicine have made great strides in the understanding and treatment of cancer, including eradicating cancer in some patients. However, there is still no cure for cancer. This is largely due to the fact that cancer is a large group of many diseases.
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A transparent cancer classifier.

Pitoyo Hartono1

  • 1Chukyo University, Japan.

Health Informatics Journal
|January 1, 2019
PubMed
Summary
This summary is machine-generated.

This study introduces a transparent neural network for histopathological analysis, enhancing cancer classification with explainable visual information. This approach aims to improve clinical trust and usability of AI in diagnostics.

Keywords:
cancer diagnosismicroarray gene expression dataneural networkvisualization

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Area of Science:

  • Artificial Intelligence
  • Medical Imaging
  • Computational Pathology

Background:

  • Neural network models show high accuracy in histopathological analysis and cancer classification.
  • Current models often function as
  • black boxes
  • , lacking transparency in their decision-making processes.
  • This lack of explainability limits the clinical adoption of AI in histopathology.

Purpose of the Study:

  • To develop a transparent neural network for histopathological analysis.
  • To provide visual explanations alongside classification decisions.
  • To enhance the clinical usability of AI in cancer diagnostics.

Main Methods:

  • Proposed a novel transparent neural network architecture.
  • Integrated auxiliary visual information to complement classification outputs.
  • Compared the proposed model's accuracy against existing classifiers.
  • Evaluated the generated visual information against dimensionality reduction techniques.

Main Results:

  • The transparent neural network achieved competitive classification accuracy.
  • The visual information provided insights into the model's decision-making process.
  • The proposed method demonstrated potential for increased usability in clinical settings.

Conclusions:

  • Transparent neural networks with visual explanations can improve the interpretability of AI in histopathology.
  • This approach addresses the "black box" problem, fostering greater trust and adoption in clinical practice.
  • The study highlights the potential of explainable AI for advancing cancer diagnostics.