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

Concepts and Prototypes

Concepts and Prototypes

The human nervous system handles vast amounts of information by translating sensory stimuli into neural impulses, which the brain processes, creating thoughts expressed through language or stored as memories. The brain also synthesizes information from emotions and memories, which significantly influence thoughts and behaviors. This intricate process creates a comprehensive mental picture.
The brain organizes this information using concepts, which are mental categories grouping linguistic data,...

You might also read

Related Articles

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

Sort by

Same author

Two-stage robust 3D CTA-2D DSA alignment via vascular-aware rigid and pyramid-based hierarchical non-rigid registration.

Medical image analysis·2026

Same author

Association between deep learning-based coronary artery calcium score on non-gated chest CT and progression of chronic kidney disease: a retrospective observational cohort study.

BMC medical imaging·2026

Same author

Heart rate variability, unstable coronary plaques, and cardiovascular outcomes.

European heart journal·2026

Same author

Metabolic Dysfunction and Coronary Plaque Vulnerability: The Predictive Role of Insulin Resistance Indices in Cardiovascular Outcomes.

MedComm·2026

Same author

Deciphering age- and sex-specific patterns of coronary artery atherosclerosis from a large Chinese cohort.

Nature communications·2025

Same author

Habitat-derived radiomics analysis based on dual lesion for the prediction of microvascular invasion in bifocal hepatocellular carcinoma.

BMC medical imaging·2025

Same journal

Embracing intra-class heterogeneity for semi-supervised medical image segmentation: From diversity to precision.

Medical image analysis·2026

Same journal

Real-time patient-specific microwave ablation zone prediction via a unified bioheat solver and MRI-informed perturbation learning.

Medical image analysis·2026

Same journal

Generative morphodynamic forecasting enables robust zero-shot volumetric medical segmentation.

Medical image analysis·2026

Same journal

ContiMorph: An unsupervised learning framework for cardiac motion tracking with time-continuous diffeomorphism.

Medical image analysis·2026

Same journal

MedP-CLIP: Medical CLIP with region-aware prompt integration.

Medical image analysis·2026

Same journal

Multi-organ guided diagnosis of mild cognitive impairment via hierarchical alignment and knowledge distillation.

Medical image analysis·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Apr 23, 2026

Author Spotlight: Bridging Gaps in Anatomy and Establishing a Foundation for Algorithmic Studies

Author Spotlight: Bridging Gaps in Anatomy and Establishing a Foundation for Algorithmic Studies

Published on: December 15, 2023

A hierarchical prompt and prototype learning framework for brain disorder classification.

Yuxiao Liu¹, Kaicong Sun², Yaping Wu³

¹School of Biomedical Engineering & State Key Laboratory of Advanced Medical Materials & Devices, ShanghaiTech University, Shanghai, 201210, China; Lingang Laboratory, Shanghai, 200031, China.

Medical Image Analysis

|April 21, 2026

Summary

This summary is machine-generated.

This study introduces a Hierarchical Prompt and Prototype Learning (HP2L) framework for brain disorder (BD) diagnosis, improving accuracy by mimicking radiologists' multi-level diagnostic processes. HP2L enhances diagnosis for both common and rare BDs, outperforming existing methods.

Keywords:

Explanation analysis Hierarchical classification Multi-level framework Prompt learning Prototype learning

More Related Videos

Translational Brain Mapping at the University of Rochester Medical Center: Preserving the Mind Through Personalized Brain Mapping

Translational Brain Mapping at the University of Rochester Medical Center: Preserving the Mind Through Personalized Brain Mapping

Published on: August 12, 2019

Event Related Potentials ERPs and other EEG Based Methods for Extracting Biomarkers of Brain Dysfunction: Examples from Pediatric Attention Deficit/Hyperactivity Disorder ADHD

Event Related Potentials ERPs and other EEG Based Methods for Extracting Biomarkers of Brain Dysfunction: Examples from Pediatric Attention Deficit/Hyperactivity Disorder ADHD

Published on: March 12, 2020

Related Experiment Videos

Last Updated: Apr 23, 2026

Author Spotlight: Bridging Gaps in Anatomy and Establishing a Foundation for Algorithmic Studies

Author Spotlight: Bridging Gaps in Anatomy and Establishing a Foundation for Algorithmic Studies

Published on: December 15, 2023

Translational Brain Mapping at the University of Rochester Medical Center: Preserving the Mind Through Personalized Brain Mapping

Translational Brain Mapping at the University of Rochester Medical Center: Preserving the Mind Through Personalized Brain Mapping

Published on: August 12, 2019

Event Related Potentials ERPs and other EEG Based Methods for Extracting Biomarkers of Brain Dysfunction: Examples from Pediatric Attention Deficit/Hyperactivity Disorder ADHD

Event Related Potentials ERPs and other EEG Based Methods for Extracting Biomarkers of Brain Dysfunction: Examples from Pediatric Attention Deficit/Hyperactivity Disorder ADHD

Published on: March 12, 2020

Area of Science:

Artificial Intelligence
Medical Imaging
Computational Neuroscience

Background:

Accurate brain disorder (BD) diagnosis is clinically challenging.
Current deep learning methods often use a one-step diagnosis, risking misdiagnosis for complex or rare BDs.
Radiologists employ a step-wise, multi-level diagnostic approach.

Purpose of the Study:

To develop a novel deep learning framework, Hierarchical Prompt and Prototype Learning (HP2L), that emulates multi-level diagnostic procedures for improved BD diagnosis.
To capture and leverage the hierarchical relationships among 23 BDs across coarse, intermediate, and fine-grained diagnostic levels.
To enhance diagnostic accuracy and interpretability in brain disorder classification.

Main Methods:

Introduced the HP2L framework integrating a Hierarchical Prompting Vision Transformer (ViT) backbone for coarse-to-fine feature extraction.
Employed Prompt Learning with optimizable prompt tokens encoding diagnostic knowledge for hierarchical classification.
Utilized Prototype Learning to enrich prompt tokens with BD-specific prototypes, enhancing diagnostic performance.

Main Results:

HP2L achieved a balanced accuracy of 88.43% on 54,360 subjects across six multi-center datasets.
Outperformed state-of-the-art methods by 8.42 percentage points, demonstrating superior performance for common and long-tail BDs.
Improved model interpretability by aligning predictions and attention visualizations with clinical hierarchical reasoning.

Conclusions:

The HP2L framework effectively emulates clinical multi-level diagnostic processes for enhanced brain disorder diagnosis.
HP2L demonstrates significant improvements in accuracy and interpretability compared to existing methods, particularly for challenging BDs.
The proposed approach offers a promising direction for developing more robust and reliable AI-driven diagnostic tools in clinical practice.