Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Video

Updated: Sep 1, 2025

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model
07:13

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

Published on: April 18, 2025

184

Double-Balanced Loss for Imbalanced Colorectal Lesion Classification.

Chang Yu1, Wei Sun1, Qilin Xiong1

  • 1Information Engineering College, Shanghai Maritime University, Shanghai 201306, China.

Computational and Mathematical Methods in Medicine
|August 18, 2022
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

Reducing Line Loss01:18

Reducing Line Loss

189
In a three-phase circuit, line loss is an indicator of energy dissipated as heat due to the resistance of transmission lines. To address this, incorporating transformers into the system—a step-up transformer at the source and a step-down transformer at the load—is a strategic solution. Two three-phase transformers are introduced to improve this.
With a step-up transformer at the source, the voltage is increased, thereby reducing the current in the transmission lines since power loss...
189

You might also read

Related Articles

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

Sort by
Same author

White-Light Endoscopic Colorectal Lesion Detection Based on Improved YOLOv5.

Computational and mathematical methods in medicine·2022
Same author

Application of Deep Learning for Early Screening of Colorectal Precancerous Lesions under White Light Endoscopy.

Computational and mathematical methods in medicine·2020
See all related articles

This study introduces a novel double-balanced loss function to improve deep learning models for colorectal cancer detection from colonoscopy images. This method addresses data imbalance, enhancing diagnostic accuracy for better patient outcomes.

Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Oncology

Background:

  • Colorectal cancer (CRC) is a global health concern with improved survival rates linked to early detection.
  • Deep learning-based computer-aided diagnosis (CADx) systems show promise in analyzing colonoscopy images to reduce diagnostic errors.
  • A key challenge in medical image analysis is the inherent class imbalance in training datasets, which can compromise deep learning model performance.

Purpose of the Study:

  • To develop and evaluate a novel loss function designed to mitigate the impact of data imbalance in deep learning models for colorectal cancer detection.
  • To address both sample size and sample difficulty imbalances within medical image datasets.
  • To enhance the accuracy and reliability of computer-aided diagnosis systems for colorectal cancer screening.

More Related Videos

Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery
06:46

Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery

Published on: September 27, 2024

359
Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
13:44

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Published on: August 30, 2013

43.0K

Related Experiment Videos

Last Updated: Sep 1, 2025

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model
07:13

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

Published on: April 18, 2025

184
Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery
06:46

Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery

Published on: September 27, 2024

359
Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
13:44

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Published on: August 30, 2013

43.0K

Main Methods:

  • A new loss function, termed the "double-balanced loss function," was proposed for deep learning models.
  • This loss function incorporates considerations for sample size and sample difficulty into the loss calculation.
  • The proposed method was integrated into a deep learning framework for the medical diagnosis of colorectal cancer using colonoscopy images.

Main Results:

  • The double-balanced loss function demonstrated superior performance in handling imbalanced classification tasks involving colorectal medical images.
  • Experimental validation using three distinct colorectal white-light endoscopic image datasets confirmed the effectiveness of the proposed approach.
  • The novel loss function improved the impact of datasets on classification accuracy, outperforming existing methods on imbalanced data.

Conclusions:

  • The proposed double-balanced loss function is an effective strategy for improving deep learning model performance on imbalanced colorectal cancer image datasets.
  • This approach offers a valuable tool for enhancing the accuracy of computer-aided diagnosis in gastroenterology.
  • Further development and application of balanced loss functions can significantly advance automated medical image analysis and diagnostics.