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: Aug 23, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

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

Published on: November 30, 2022

2.9K

Weakly Supervised Learning using Attention gates for colon cancer histopathological image segmentation.

A Ben Hamida1, M Devanne2, J Weber2

  • 1ICube, University of Strasbourg, France.

Artificial Intelligence in Medicine
|November 3, 2022
PubMed
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

Regorafenib and metronomic capecitabine, cyclophosphamide, and aspirin in refractory metastatic colorectal cancer: results from the REPROGRAM-01 single-arm phase II trial.

ESMO gastrointestinal oncology·2026
Same author

HPV circulating tumor DNA to monitor response to pembrolizumab and vorinostat combination in patients with advanced HPV-related squamous-cell carcinomas.

ESMO open·2025
Same author

Clinical description and development of a prognostic score for neurofibromatosis type 1 (NF1)-associated GISTs: a retrospective study from the NETSARC.

ESMO open·2025
Same author

First-in-human study of AMG 193, an MTA-cooperative PRMT5 inhibitor, in patients with MTAP-deleted solid tumors: results from phase I dose exploration.

Annals of oncology : official journal of the European Society for Medical Oncology·2024
Same author

Real-world comparison of chemo-immunotherapy and chemotherapy alone in the treatment of extensive-stage small-cell lung cancer.

Respiratory medicine and research·2024
Same author

The Management of Persistent Distal Occlusions after Mechanical Thrombectomy and Thrombolysis: An Inter- and Intrarater Agreement Study.

AJNR. American journal of neuroradiology·2024

Deep learning models, specifically enhanced Att-UNet architectures, achieve high accuracy in segmenting colon cancer histopathology images. These novel methods address data limitations and outperform existing approaches for digital pathology tasks.

Area of Science:

  • Artificial Intelligence
  • Digital Pathology
  • Computational Biology

Background:

  • Deep learning methods have revolutionized various applications, including digital pathology for tumor diagnosis and prognosis.
  • Classical machine learning methods struggle with Whole Slide Images (WSI) due to their large size, high resolution, and limited annotated samples, hindering generalization.
  • Traditional methods exhibit poor generalization across different tasks and data types in histopathological image analysis.

Approach:

  • This study explores deep learning models, specifically UNet and Att-UNet, for colon cancer WSI segmentation in sparsely annotated datasets.
  • Novel enhanced Att-UNet models are introduced, optimizing skip connections and spatial attention gate placement for improved feature learning.
  • A multi-step training strategy is proposed to address data scarcity, sparse annotations, and class imbalance in colon cancer datasets.
Keywords:
Attention gatesColon cancerDeep LearningDigital pathologyImage segmentationWeak supervision

More Related Videos

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

483

Related Experiment Videos

Last Updated: Aug 23, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

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

Published on: November 30, 2022

2.9K
Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

483

Key Points:

  • Spatial attention gates enhance training by preventing irrelevant feature learning, leading to more robust segmentation.
  • The Alter-AttUNet model offers a balance between accuracy and network efficiency, achieving 95.88% accuracy on the AiCOLO colon cancer dataset.
  • Proposed methods outperform state-of-the-art approaches on both proprietary (AiCOLO) and public datasets (NCT-CRC-HE-100K, CRC-5000, Warwick).

Conclusions:

  • The developed Alter-AttUNet model provides a robust and accurate solution for histopathological image segmentation in digital pathology.
  • The multi-step training strategy effectively handles sparse annotations and class imbalance, crucial for real-world datasets.
  • The findings demonstrate the potential of advanced deep learning techniques to improve the efficiency and accuracy of cancer diagnosis from histopathological images.