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 Concept Videos

You might also read

Related Articles

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

Sort by
Same author

Immunity-and-matrix-regulatory cells promote hyaline-like cartilage repair in osteoarthritis.

Bioactive materials·2026
Same author

Optimization of Thulium Fiber Laser Lithotripsy Efficacy Based on Multidimensional Parameter Combination Strategies.

European urology open science·2026
Same author

Revelations of pancreatic cancer treated by high-intensity focused ultrasound.

Discover oncology·2026
Same author

Differentiating benign from malignant pulmonary nodules in the context of bronchiectasis: a retrospective study.

Annals of medicine·2026
Same author

Stiff-yet-tough glassy hydrogels for tendon rupture repair.

Bioactive materials·2026
Same author

Uncovering the realities of suicidal ideation in older patients following lung cancer diagnosis: an interpretive phenomenological qualitative study.

Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer·2026
Same journal

Deep Learning Based Framework for Detection and Classification of Leukemia Using Microscopic Images.

Microscopy research and technique·2026
Same journal

Externally Controlled In Situ SEM: Multi-Rate Scanning With Signal Regulation and Spatiotemporal Fusion.

Microscopy research and technique·2026
Same journal

In Situ TEM Observation of Phase Transformation Nucleation at the Near-Surface of Synthetic Aragonite.

Microscopy research and technique·2026
Same journal

Morpho-Anatomical and HPTLC Investigations of Lysimachia nummularia L. (Primulaceae) Grown in Switzerland.

Microscopy research and technique·2026
Same journal

Macroscopic, Histological and Ultrastructural Features of the Tongue of the Anatolian Wild Boar (Sus scrofa libycus).

Microscopy research and technique·2026
Same journal

Ultrastructural Insights Into the Reproductive Anatomy and Eggs of Cotton Pink Bollworm, Pectinophora gossypiella Saunders (Lepidoptera: Gelechiidae).

Microscopy research and technique·2026
See all related articles

Related Experiment Video

Updated: Jan 17, 2026

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

7.9K

HAMIL: Hierarchical Attention Multi-Instance Learning for Label-Free Colorectal Cancer Typing.

Zhaoyi Ye1, Sisi Mei2, Liang Tao2

  • 1School of Integrated Circuits, Wuhan University, Wuhan, China.

Microscopy Research and Technique
|September 23, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a novel Hierarchical Attention Multi-Instance Learning (HAMIL) method for label-free colorectal cancer (CRC) typing. HAMIL achieves 86.30% F1 score, offering a new pathway for efficient clinical diagnosis.

Keywords:
colorectal cancerhierarchical attentionhigh‐throughput cell imageoptical time‐stretch imaging

More Related Videos

Label-Free Identification of Lymphocyte Subtypes Using Three-Dimensional Quantitative Phase Imaging and Machine Learning
08:58

Label-Free Identification of Lymphocyte Subtypes Using Three-Dimensional Quantitative Phase Imaging and Machine Learning

Published on: November 19, 2018

13.0K
Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images
08:20

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Published on: October 27, 2023

2.5K

Related Experiment Videos

Last Updated: Jan 17, 2026

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

7.9K
Label-Free Identification of Lymphocyte Subtypes Using Three-Dimensional Quantitative Phase Imaging and Machine Learning
08:58

Label-Free Identification of Lymphocyte Subtypes Using Three-Dimensional Quantitative Phase Imaging and Machine Learning

Published on: November 19, 2018

13.0K
Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images
08:20

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Published on: October 27, 2023

2.5K

Area of Science:

  • Oncology
  • Biomedical Imaging
  • Machine Learning

Background:

  • Colorectal cancer (CRC) is a leading gastrointestinal malignancy requiring advanced diagnostic tools.
  • Current pathological imaging for CRC diagnosis is time-consuming and requires expert annotation.
  • Analyzing the tumor microenvironment is crucial for understanding CRC progression.

Purpose of the Study:

  • To develop a label-free method for colorectal cancer typing using Hierarchical Attention Multi-Instance Learning (HAMIL).
  • To integrate optical time-stretch (OTS) imaging with microfluidic cell focusing for high-throughput cell image acquisition.
  • To construct a high-throughput CRC typing dataset for method validation.

Main Methods:

  • Development of a high-throughput cell image acquisition system using optical time-stretch (OTS) imaging and microfluidic cell focusing.
  • Construction of a CRC typing dataset comprising 363,931 cell images from 10 clinical samples.
  • Implementation of HAMIL, featuring instance attention for single-cell analysis and bag attention for population-level characteristics.

Main Results:

  • The HAMIL method achieved an 86.30% F1 score in CRC typing.
  • HAMIL outperformed eight other advanced Multiple Instance Learning (MIL) methods.
  • The model effectively captured tumor heterogeneity and microenvironment characteristics.

Conclusions:

  • HAMIL provides an effective, label-free approach for clinical CRC typing.
  • The integration of OTS imaging and HAMIL enables efficient analysis of cellular populations.
  • This study establishes a new pathway for high-throughput analysis in gastrointestinal oncology.