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Related Concept Videos

The Scientific Method02:40

The Scientific Method

Research is what makes the difference between facts and opinions. Facts are observable realities, and opinions are personal judgments, conclusions, or attitudes that may or may not be accurate. In the scientific community, facts can be established only using evidence collected through empirical research.
Systematic Error: Methodological and Sampling Errors01:15

Systematic Error: Methodological and Sampling Errors

In the case of systematic errors, the sources can be identified, and the errors can be subsequently minimized by addressing these sources. According to the source, systematic errors can be divided into sampling, instrumental, methodological, and personal errors.
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Structuralism01:26

Structuralism

Structuralism, an early psychological theory developed by Wilhelm Wundt and his student Edward Bradford Titchener, sought to dissect the human mind into its most fundamental components. Wundt's groundbreaking work in his laboratory set the stage for Titchener to define structuralism's goal as cataloging the "atoms" of the mind—sensations, images, and feelings—akin to how chemists identify elements of matter.
Titchener's approach to structuralism was unique. He employed introspection, a method...
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Theoretical Approaches to Psychological Disorder

The development of psychological disorders, which are characterized by deviant, maladaptive, and personally distressing behaviors, has been explored through several theoretical approaches.
Biological approach
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Industrialized, Artificial Intelligence-guided Laser Microdissection for Microscaled Proteomic Analysis of the Tumor Microenvironment
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Deep learning for digital pathology: A critical overview of methodological framework.

Meghdad Sabouri Rad1, Junze Vincent Huang2, Mohammad Mehdi Hosseini1

  • 1SUNY Upstate Medical University, Syracuse, NY 13210, USA.

Journal of Pathology Informatics
|November 10, 2025
PubMed
Summary

Deep learning significantly enhances digital pathology by automating complex analyses of histopathological data. This framework reveals intricate patterns in whole-slide images, improving diagnostic precision and scalability.

Keywords:
Deep learning frameworkDeep neural networksDigital pathologyMachine learning framework

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Area of Science:

  • Digital Pathology
  • Computational Pathology
  • Artificial Intelligence in Medicine

Background:

  • Deep learning frameworks are revolutionizing digital pathology.
  • Automating complex tasks and pattern recognition in histopathology is crucial.
  • High-dimensional whole-slide image analysis requires advanced methodologies.

Purpose of the Study:

  • To present a comprehensive deep learning framework for computational pathology.
  • To highlight recent advancements in the field.
  • To critically examine mathematical innovations and compare various models.

Main Methods:

  • Utilizing deep learning frameworks for histopathological data analysis.
  • Applying advanced methodologies to whole-slide images.
  • Conducting a comparative analysis of different computational pathology models.

Main Results:

  • Deep learning provides exceptional accuracy and scalability in digital pathology.
  • The framework facilitates precise analysis of high-dimensional data.
  • Significant improvements in computational pathology are demonstrated.

Conclusions:

  • Deep learning frameworks are transforming digital pathology.
  • Ongoing mathematical innovations are driving field-wide improvements.
  • The presented framework offers a robust approach to computational pathology.