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: Jun 28, 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

Organ masks applied in feature space improve weakly supervised scan-level CT classification.

Lena Philipp¹, Han Liu², Sasa Grbic²

¹Diagnostic Image Analysis Group, Radboud University Medical Center, Nijmegen, 6525, The Netherlands. lena.philipp@radboudumc.nl.

Scientific Reports

|June 26, 2026

Summary

This summary is machine-generated.

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

Beyond the LUMIR challenge: The pathway to foundational registration models.

Medical image analysis·2026

Same author

Predictors of Pathological Complete Response and Patient-Reported Outcomes During Neoadjuvant Chemotherapy in Early Breast Cancer: A Single-Center Retrospective Cohort Study.

Cureus·2026

Same author

A longitudinal whole-body CT dataset with manually annotated tumor lesions.

Scientific data·2026

Same author

PatchCLIP enables region specific contrastive health record and image joint training with patch embedding loss.

Scientific reports·2026

Same author

Deep learning-based pulmonary nodule risk assessment outperforms established malignancy risk scores in lung cancer screening.

Radiology advances·2026

Same author

The Performance of Protein Meal from <i>Hermetia illucens</i> Larvae in Hetero-Clarias Hybrid Farming.

Insects·2025

Same journal

Turbulent flow in a vortex separator with a directed pipe inlet.

Scientific reports·2026

Same journal

Systematic characteristic evaluation of clay-based cementitious material derived from calcium carbide residue and waste tile powder.

Scientific reports·2026

Same journal

Retraction Note: Improvement of a rapid diagnostic application of monoclonal antibodies against avian influenza H7 subtype virus using Europium nanoparticles.

Scientific reports·2026

Same journal

Applying large language models to spam detection in the Kazakh low-resource language setting.

Scientific reports·2026

Same journal

An open-source 3D printing system enabling in-situ freeze-thaw processing of hydrogels.

Scientific reports·2026

Same journal

An enhanced EfficientNet framework for automated waste classification using cosine annealing and label smoothing.

Scientific reports·2026

See all related articles

Feature-space cropping using organ masks improves weakly supervised classification of computed tomography (CT) scans. This method enhances anatomical focus without retraining foundation models, boosting performance in medical imaging analysis.

Area of Science:

Medical Imaging
Artificial Intelligence in Medicine

Background:

Automated analysis of computed tomography (CT) scans is crucial for medical imaging research.
Current classification methods often lack spatial information, and using pretrained models with input-space cropping can degrade performance by altering the input distribution.

Purpose of the Study:

To investigate the efficacy of organ masks in providing scalable anatomical guidance for weakly supervised classification using a frozen self-supervised Swin Transformer.
To compare input-space organ centering with feature-space cropping for CT scan classification.

Main Methods:

Two strategies were evaluated: input-space organ centering and feature-space cropping, which applies organ masks to intermediate feature maps before pooling.
The study utilized three datasets and seven binary classification tasks with a frozen self-supervised Swin Transformer.

Keywords:

Classification Computed tomography Vision transformer Weakly supervised learning

Related Experiment Videos

Last Updated: Jun 28, 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

Main Results:

Feature-space cropping matched or improved performance compared to full-image baselines across datasets and tasks.
Feature-space cropping achieved a pooled improvement of 0.018 in the area under the receiver operating characteristic curve, with significant gains in liver lesion and pericardial effusion classification.
Input-space centering showed task-dependent effects, and tighter crops in ablation experiments reduced performance, emphasizing the importance of peri-organ context.

Conclusions:

Feature-level anatomical guidance via feature-space cropping is an efficient strategy to enhance weakly supervised CT classification without retraining the encoder.
Feature-space cropping reduces embedding dimensionality without performance loss and preserves input distribution.
This approach offers a scalable method for improving anatomical focus in medical image analysis.