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

Search research articles

Related Experiment Video

Updated: Jan 15, 2026

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

Elevating adversarial robustness by contrastive multitasking defence in medical image segmentation.

Sneha Shukla¹, Puneet Gupta¹

¹Department of Computer Science and Engineering, Indian Institute of Technology Indore, Indore, India.

Neural Networks : the Official Journal of the International Neural Network Society

|October 11, 2025

Summary

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

Unveiling the Potential of <i>p</i>-Block Bismuth in Homogeneous Electrocatalytic Proton Reduction: Secondary Coordination Sphere Control of Catalytic Activity.

Journal of the American Chemical Society·2026

Same author

Clay pots for reducing fluoride concentration in drinking water.

The Indian journal of medical research·2026

Same author

Invasive Spinal Neuromodulation for Chronic Pain in Pediatric Patients: A Scoping Review.

Pain practice : the official journal of World Institute of Pain·2026

Same author

Manganese-Catalyzed Formate Synthesis via Coupled Methanol Dehydrogenation and Bicarbonate Reduction.

Inorganic chemistry·2026

Same author

Right Atrial Myxoma With Dual Coronary Artery Feeding: A Vascular Surprise.

JACC. Case reports·2026

Same author

Ellis-van Creveld Syndrome: Common Atrium with Post-axial Polydactyly.

QJM : monthly journal of the Association of Physicians·2026

Adversarial attacks threaten Deep Learning (DL) medical image segmentation (MIS) models. Our novel CEASE defense, using contrastive and multitask learning, significantly enhances adversarial resilience in MIS models.

Area of Science:

Artificial Intelligence
Medical Imaging
Computer Vision

Background:

Deep Learning (DL) models are crucial for Medical Image Segmentation (MIS).
Adversarial attacks degrade DL-based MIS model performance and robustness.
Existing defenses are less effective in the medical domain.

Purpose of the Study:

To propose a novel defense strategy, CEASE (Contrastive Multitasking Defense), to enhance adversarial resilience in DL-based MIS models.
To investigate the efficacy of contrastive and multitask learning for improving robustness against adversarial attacks in medical imaging.

Main Methods:

CEASE integrates contrastive learning to ensure similar feature learning for clean, adversarial, and augmented samples.
A multitask learning approach is employed to generate generic feature representations and improve robustness.

Keywords:

Adversarial defence Contrastive learning Deep learning Medical image segmentation Multitask model Robustness

More Related Videos

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

Related Experiment Videos

Last Updated: Jan 15, 2026

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

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

A fusion-based defense consolidates contrastive and multitask learning for optimal adversarial resilience.

Main Results:

CEASE significantly enhances adversarial resilience in state-of-the-art MIS models.
The proposed defense mitigates adversarial attacks, achieving up to 0% attack success rate.
CEASE demonstrates modest performance gains while improving robustness.

Conclusions:

CEASE effectively bridges the gap in adversarial defense for medical image segmentation.
The integration of contrastive and multitask learning offers a promising direction for robust DL models in healthcare.
CEASE provides a robust solution for defending medical image segmentation models against adversarial attacks.