Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Machines

Machines

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.
A free-body diagram of the...

Machines: Problem Solving II

Machines: Problem Solving II

Machines are complex structures consisting of movable, pin-connected multi-force members that work together to transmit forces. Consider a lifting tong carrying a 100 kg load. It comprises movable sections DAF and CBG linked together with member AB.

Machines: Problem Solving I

Machines: Problem Solving I

A toggle clamp is a mechanical device commonly used for holding and clamping objects in various applications, such as woodworking, metalworking, and assembly operations. Consider a toggle clamp subjected to a force of 200 N at the handle. The vertical clamping force can be calculated, provided the dimensions of the toggle clamp are known.
The toggle clamp system is a machine structure consisting of movable, pin-connected multi-force members that form a stabilized system to transmit forces. The...

Avoidance Learning and Learned Helplessness

Avoidance Learning and Learned Helplessness

Avoidance learning and learned helplessness are critical concepts in understanding behavioral responses to negative stimuli.
Avoidance learning occurs when an organism learns that a specific behavior can prevent an unpleasant outcome. For example, a student who receives a bad grade may start studying harder to avoid future poor grades. This behavior persists even when the negative outcome is no longer present. Avoidance learning is powerful because it maintains behavior in the absence of the...

Associative Learning

Associative Learning

Associative learning is a fundamental concept in behavioral psychology, wherein a connection is established between two stimuli or events, leading to a learned response. This process is critical in understanding how behaviors are acquired and modified. Conditioning, the mechanism through which associations are formed, can be divided into two main types: classical conditioning and operant conditioning, each elucidating different aspects of associative learning.
Classical conditioning, also known...

Purposive Learning

Purposive Learning

E. C. Tolman emphasized the purposiveness of behavior — the idea that much of our behavior is goal-directed. For instance, employees who aim for a promotion work diligently to meet their targets. Tolman argued that when classical conditioning and operant conditioning occur, the organism acquires certain expectations. In classical conditioning, a child might fear a dog because they expect it to bite. In operant conditioning, a person might consistently work overtime because they expect a...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Topological modeling of gene expression in the brain with Huntington's disease reveals selective disruption of co-expression network.

Scientific reports·2026

Same author

Artificial intelligence-assisted screening for NTRK fusion-positive salivary gland tumors: A novel digital pathology workflow.

Human pathology·2026

Same author

Role of Upregulated Mucin 21 in Detached Lung Cancer Cells.

Pathology international·2026

Same author

TANC1::HTRA1 and KPNA4::WWTR1 fusions in non-vestibular intracranial schwannomas.

Acta neuropathologica·2026

Same author

Cooperative response loops enhanced by histamine signaling during tumor-neutrophil crosstalk in pleural mesothelioma.

Cell reports·2026

Same author

Impact of genetic profiles on periventricular anastomosis following bypass surgery in moyamoya disease.

Neurosurgical review·2026

Same journal

Functional Genomic Evidence for Candidate Small Viral RNA-Mediated Epigenetic Interference in SARS-CoV-1 and SARS-CoV-2.

Computational and structural biotechnology journal·2026

Same journal

From Pixels to Patterns: A Multidimensional Framework to Decode Cytoskeletal Organization.

Computational and structural biotechnology journal·2026

Same journal

A Large Concept Model for Mechanistic Simulation of Disease Trajectories: A Hypothesis-Generating Exemplar for Pediatric Acute Lymphoblastic Leukemia.

Computational and structural biotechnology journal·2026

Same journal

Adversarial Sequence Mutations in AlphaFold and ESMFold Reveal Nonphysical Structural Invariance, Confidence Failures, and Concerns for Protein Design.

Computational and structural biotechnology journal·2026

Same journal

High-Throughput Prediction of Protein-Protein Interactions Uncovers Hidden Molecular Networks in Biosynthetic Gene Clusters.

Computational and structural biotechnology journal·2026

Same journal

A Region-Aware Structured Framework Improves Prediction of Gene Expression from DNA Methylation.

Computational and structural biotechnology journal·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Feb 4, 2026

Label-free, High-Resolution 3D Imaging and Machine Learning Analysis of Intestinal Organoids via Low-Coherence Holotomography

Label-free, High-Resolution 3D Imaging and Machine Learning Analysis of Intestinal Organoids via Low-Coherence Holotomography

Published on: August 12, 2025

Machine Learning Methods for Histopathological Image Analysis.

Daisuke Komura¹, Shumpei Ishikawa¹

¹Department of Genomic Pathology, Medical Research Institute, Tokyo Medical and Dental University, Tokyo, Japan.

Computational and Structural Biotechnology Journal

|October 3, 2018

Summary

This summary is machine-generated.

Digital pathology image analysis using machine learning offers computer-aided diagnosis. This review covers applications, challenges, and solutions for analyzing digital histopathological images with machine learning algorithms.

Keywords:

Computer assisted diagnosis Deep learning Digital image analysis Histopathology Machine learning Whole slide images

More Related Videos

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

Author Spotlight: Enhancing PSC-to-Functional Cell Differentiation Using ML Models Based on Live-Cell Bright-Field Imaging

Author Spotlight: Enhancing PSC-to-Functional Cell Differentiation Using ML Models Based on Live-Cell Bright-Field Imaging

Published on: October 4, 2024

Related Experiment Videos

Last Updated: Feb 4, 2026

Label-free, High-Resolution 3D Imaging and Machine Learning Analysis of Intestinal Organoids via Low-Coherence Holotomography

Label-free, High-Resolution 3D Imaging and Machine Learning Analysis of Intestinal Organoids via Low-Coherence Holotomography

Published on: August 12, 2025

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

Author Spotlight: Enhancing PSC-to-Functional Cell Differentiation Using ML Models Based on Live-Cell Bright-Field Imaging

Author Spotlight: Enhancing PSC-to-Functional Cell Differentiation Using ML Models Based on Live-Cell Bright-Field Imaging

Published on: October 4, 2024

Area of Science:

Digital pathology
Computational pathology
Medical imaging analysis

Background:

Increasing volume of digital histopathological images necessitates advanced analysis techniques.
Machine learning (ML) is pivotal for developing computer-aided diagnosis (CAD) systems in digital pathology.
Challenges exist in the analysis of digital pathological images and related tasks.

Purpose of the Study:

To review the application of ML algorithms in digital pathological image analysis.
To identify and discuss specific problems encountered in this field.
To propose potential solutions for these identified challenges.

Main Methods:

Literature review of ML applications in digital pathology.
Analysis of common issues in digital histopathological image analysis.
Synthesis of proposed solutions and future directions.

Main Results:

Overview of diverse ML techniques applied to digital pathology tasks.
Identification of key challenges such as data heterogeneity, annotation, and model interpretability.
Discussion of strategies including data augmentation, transfer learning, and explainable AI.

Conclusions:

ML holds significant promise for advancing computer-aided diagnosis in digital pathology.
Addressing specific challenges is crucial for the effective implementation of ML solutions.
Further research is needed to optimize ML models for robust and reliable pathological image analysis.