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

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

Corrigendum: Referenceless MR thermometry-a comparison of five methods (2017<i>Phys. Med. Biol</i>.<b>62</b>1-16).

Physics in medicine and biology·2026

Same author

Mimicking Natural Photosynthesis: Bromide-Mediated Photocatalysis for Spatially Decoupled Olefin Epoxidation and Hydrogen Evolution.

Journal of the American Chemical Society·2026

Same author

Hybrid Biochar from Corn Stover and Sewage Sludge for VOCs Adsorption: A Sustainable Waste Utilization Approach.

Toxics·2026

Same author

Predicting early-stage breast cancer disease-free survival and adjuvant therapy benefit from multimodal information using deep learning.

NPJ breast cancer·2026

Same author

Lumi-Guide: An Artificial-Intelligence-Driven Multimodal Framework for Optimizing Personalized Neoadjuvant Therapy Decision-Making in Luminal Breast Cancer.

Research (Washington, D.C.)·2026

Same author

Development and validation of an MRI-based deep learning system for triple-class ER expression classification in breast cancer: a large-scale multicenter study.

BMC medicine·2026

Same journal

Embracing intra-class heterogeneity for semi-supervised medical image segmentation: From diversity to precision.

Medical image analysis·2026

Same journal

Real-time patient-specific microwave ablation zone prediction via a unified bioheat solver and MRI-informed perturbation learning.

Medical image analysis·2026

Same journal

Generative morphodynamic forecasting enables robust zero-shot volumetric medical segmentation.

Medical image analysis·2026

Same journal

ContiMorph: An unsupervised learning framework for cardiac motion tracking with time-continuous diffeomorphism.

Medical image analysis·2026

Same journal

MedP-CLIP: Medical CLIP with region-aware prompt integration.

Medical image analysis·2026

Same journal

Multi-organ guided diagnosis of mild cognitive impairment via hierarchical alignment and knowledge distillation.

Medical image analysis·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Sep 20, 2025

Quantitation of Protein Expression and Co-localization Using Multiplexed Immuno-histochemical Staining and Multispectral Imaging

Quantitation of Protein Expression and Co-localization Using Multiplexed Immuno-histochemical Staining and Multispectral Imaging

Published on: April 8, 2016

Multi-layer pseudo-supervision for histopathology tissue semantic segmentation using patch-level classification

Chu Han¹, Jiatai Lin², Jinhai Mai³

¹Department of Radiology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, Guangdong 510080, China; Guangdong Cardiovascular Institute, Guangzhou, Guangdong 510080, China; Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, Guangdong 510080, China.

Medical Image Analysis

|June 7, 2022

Summary

This summary is machine-generated.

This study introduces a novel method for tissue semantic segmentation using only patch-level labels, significantly reducing annotation time and effort in computational pathology. The approach achieves results comparable to fully-supervised methods with minimal performance loss.

Keywords:

Computational pathology Pseudo mask generation Tissue segmentation Weakly-supervised learning

More Related Videos

Volume Segmentation and Analysis of Biological Materials Using SuRVoS Super-region Volume Segmentation Workbench

Volume Segmentation and Analysis of Biological Materials Using SuRVoS Super-region Volume Segmentation Workbench

Published on: August 23, 2017

Industrialized, Artificial Intelligence-guided Laser Microdissection for Microscaled Proteomic Analysis of the Tumor Microenvironment

Industrialized, Artificial Intelligence-guided Laser Microdissection for Microscaled Proteomic Analysis of the Tumor Microenvironment

Published on: June 3, 2022

Related Experiment Videos

Last Updated: Sep 20, 2025

Quantitation of Protein Expression and Co-localization Using Multiplexed Immuno-histochemical Staining and Multispectral Imaging

Quantitation of Protein Expression and Co-localization Using Multiplexed Immuno-histochemical Staining and Multispectral Imaging

Published on: April 8, 2016

Volume Segmentation and Analysis of Biological Materials Using SuRVoS Super-region Volume Segmentation Workbench

Volume Segmentation and Analysis of Biological Materials Using SuRVoS Super-region Volume Segmentation Workbench

Published on: August 23, 2017

Industrialized, Artificial Intelligence-guided Laser Microdissection for Microscaled Proteomic Analysis of the Tumor Microenvironment

Industrialized, Artificial Intelligence-guided Laser Microdissection for Microscaled Proteomic Analysis of the Tumor Microenvironment

Published on: June 3, 2022

Area of Science:

Computational pathology
Digital pathology
Medical image analysis

Background:

Tissue-level semantic segmentation is crucial for computational pathology.
Fully-supervised models require expensive, time-consuming pixel-level annotations on whole slide images.
Reducing annotation effort is essential for broader adoption of computational pathology tools.

Purpose of the Study:

To develop a tissue semantic segmentation method using only patch-level classification labels.
To decrease annotation efforts in histopathology image analysis.
To introduce a new weakly-supervised semantic segmentation dataset for lung adenocarcinoma (LUAD-HistoSeg).

Main Methods:

A two-step model comprising classification and segmentation phases.
Utilizing a Class Activation Mapping (CAM)-based model for pseudo-mask generation from patch-level labels.
Implementing Multi-Layer Pseudo-Supervision for tissue semantic segmentation.

Main Results:

The proposed model outperforms five state-of-the-art weakly-supervised semantic segmentation (WSSS) approaches.
Achieved comparable quantitative and qualitative results to fully-supervised models, with only a ~2% gap in MIoU and FwIoU.
Patch-level labeling reduced annotation time from hours to minutes compared to manual labeling.

Conclusions:

Patch-level labels are sufficient for achieving high-performance tissue semantic segmentation.
The proposed WSSS method significantly reduces annotation burden in computational pathology.
The developed model and dataset facilitate advancements in automated histopathology analysis.