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

You might also read

Related Articles

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

Sort by

Same author

Association between intrarenal venous flow and postoperative acute kidney injury in cardiac surgery patients following cardiopulmonary bypass: a prospective cohort study.

BMC cardiovascular disorders·2026

Same author

Structural basis for TCR recognition of a Rac1 neoantigen arising from anchor residue mutation.

Journal of structural biology·2026

Same author

Hierarchically Chiral Silver Nanoclusters Mediated Enantioselective Glutathione Depletion and Intracellular Self-Assembly for Enhanced Anticancer Therapy.

Angewandte Chemie (International ed. in English)·2026

Same author

METTL3-mediated m<sup>6</sup>A modification of Smad3 affects ferroptosis in septic acute lung injury by mediating GRSF1/GPX4.

International immunopharmacology·2026

Same author

A study on quantitative analysis methods for mixed solution concentrations based on deep learning.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy·2026

Same author

Characterizing the role of an endogenous serine protease KpSub2 in recombinant collagen degradation in Komagataella phaffii.

Bioresources and bioprocessing·2026

Search research articles

Related Experiment Video

Updated: Jun 17, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

Evidence modeling for reliability learning and interpretable decision-making under multi-modality medical image

Jianfeng Zhao¹, Shuo Li²

¹School of Biomedical Engineering, Western University, London, ON, Canada.

Computerized Medical Imaging and Graphics : the Official Journal of the Computerized Medical Imaging Society

|August 8, 2024

Summary

This summary is machine-generated.

This study introduces the Contextual Discounted Evidential Network (CDE-Net) for multi-modality medical image segmentation, enhancing reliability learning and interpretable decision-making. CDE-Net effectively fuses information from different imaging types, improving segmentation accuracy and interpretability.

Keywords:

Interpretability Multi-modality Reliability learning Segmentation Visual explanation

More Related Videos

Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping

Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping

Published on: September 25, 2019

Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application

Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application

Published on: April 14, 2023

Related Experiment Videos

Last Updated: Jun 17, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping

Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping

Published on: September 25, 2019

Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application

Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application

Published on: April 14, 2023

Area of Science:

Medical Imaging
Artificial Intelligence
Computer Vision

Background:

Multi-modality medical image segmentation is vital but faces challenges in reliability assessment and decision interpretability.
Existing methods often lack transparency in how they weigh different imaging modalities.
The interpretability of decision-making processes, such as using softmax, is limited in multi-modality fusion.

Purpose of the Study:

To propose a novel approach, the Contextual Discounted Evidential Network (CDE-Net), for reliable and interpretable multi-modality medical image segmentation.
To develop a framework that learns the reliability of each imaging modality.
To enhance the interpretability of decision-making in multi-modality image fusion.

Main Methods:

The CDE-Net models semantic evidence using an evidential decision-making module with uncertainty measurement.
A contextual discounted fusion layer is employed to learn modality-specific reliability.
A multi-level loss function optimizes evidence modeling and reliability learning.

Main Results:

The CDE-Net achieved high performance in multi-modality segmentation tasks.
Average Dice scores of 0.914 for brain tumor segmentation and 0.913 for liver tumor segmentation were obtained.
The framework demonstrated interpretability through the consistency of pixel attribution maps and learned reliability coefficients.

Conclusions:

The CDE-Net offers a robust solution for reliability learning and interpretable decision-making in multi-modality medical image segmentation.
The proposed method shows significant potential for advancing artificial intelligence in medical image fusion.
The interpretability features of CDE-Net facilitate a better understanding of AI-driven medical image analysis.